new

.gitignore

@@ -0,0 +1,5 @@
+venv/
+/weights/*
+weight/genconvit_ed_inference.pth
+weight/genconvit_vae_inference.pth
+```

Dockerfile

@@ -0,0 +1,22 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies (simplified without dlib requirements)
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    ffmpeg \
+    libgl1-mesa-glx \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Command to run the application
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

LICENSE

@@ -0,0 +1,674 @@
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+                       Version 3, 29 June 2007
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+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

app.py

@@ -0,0 +1,502 @@
+import streamlit as st
+import os
+import tempfile
+import shutil
+import torch
+from huggingface_hub import hf_hub_download
+import cv2
+from PIL import Image
+import numpy as np
+import time
+import sys
+import json
+import graphviz
+import pandas as pd
+from datetime import datetime
+
+# Add a custom path for model imports
+if "model" not in sys.path:
+    sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Import your prediction functions
+from model.pred_func import (
+    load_genconvit,
+    df_face,
+    pred_vid,
+    real_or_fake,
+    set_result,
+    store_result
+)
+from model.config import load_config
+
+# Set page config
+st.set_page_config(
+    page_title="Deepfake Detection with GenConViT",
+    page_icon="🎭",
+    layout="wide"
+)
+
+# Initialize logs in session state
+if 'logs' not in st.session_state:
+    st.session_state.logs = []
+
+def add_log(message):
+    """Add a log entry with timestamp"""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    st.session_state.logs.append(f"[{timestamp}] {message}")
+
+@st.cache_resource
+def load_model_from_huggingface(model_type="both"):
+    """Load the model weights from Hugging Face Hub based on selection"""
+    config = load_config()
+    add_log("Starting model weights download from Hugging Face Hub")
+    
+    os.makedirs("weight", exist_ok=True)
+    
+    with st.spinner("Downloading model weights from Hugging Face Hub..."):
+        ed_path = hf_hub_download(
+            repo_id="Deressa/GenConViT",
+            filename="genconvit_ed_inference.pth",
+        )
+        vae_path = hf_hub_download(
+            repo_id="Deressa/GenConViT",
+            filename="genconvit_vae_inference.pth",
+        )
+        
+        shutil.copy(ed_path, "weight/genconvit_ed_inference.pth")
+        shutil.copy(vae_path, "weight/genconvit_vae_inference.pth")
+    add_log("Model weights downloaded successfully")
+
+    with st.spinner("Loading model..."):
+        if model_type == "ed":
+            model = load_genconvit(
+                config,
+                "genconvit",
+                "genconvit_ed_inference",
+                None,
+                fp16=False
+            )
+            add_log("Loaded ED Model only")
+        elif model_type == "vae":
+            model = load_genconvit(
+                config,
+                "genconvit",
+                None,
+                "genconvit_vae_inference",
+                fp16=False
+            )
+            add_log("Loaded VAE Model only")
+        else:
+            model = load_genconvit(
+                config,
+                "genconvit",
+                "genconvit_ed_inference",
+                "genconvit_vae_inference",
+                fp16=False
+            )
+            add_log("Loaded both ED and VAE Models")
+
+    return model, config
+
+def is_video(file):
+    """Check if a file is a valid video file"""
+    try:
+        cap = cv2.VideoCapture(file)
+        if not cap.isOpened():
+            return False
+        ret, frame = cap.read()
+        cap.release()
+        return ret
+    except:
+        return False
+
+def create_flowchart(stage=None):
+    """Creates a flowchart of the deepfake detection pipeline."""
+    graph = graphviz.Digraph('pipeline', graph_attr={'rankdir': 'LR', 'size': '10,15'})
+
+    stages = {
+        "upload": {"label": "Upload\nVideo", "fillcolor": "#ddeedd", "color": "#336633", "done": False},
+        "frames": {"label": "Extract\nFrames", "fillcolor": "#eef2ff", "color": "#336699", "done": False},
+        "preprocessing": {"label": "Preprocess\nFrames", "fillcolor": "#fff0ee", "color": "#996633", "done": False},
+        "model": {"label": "GenConViT\nModel", "fillcolor": "#f0e68c", "color": "#a67d3d", "done": False},
+        "results": {"label": "Results", "fillcolor": "#c0c0c0", "color": "#555555", "done": False},
+    }
+
+    if stage:
+        for key in stages:
+            if key == stage:
+                stages[key]["fillcolor"] = "#ffcc00"
+                stages[key]["color"] = "#b8860b"
+                break
+            else:
+                stages[key]["fillcolor"] = "#90ee90"
+                stages[key]["color"] = "#006400"
+                stages[key]["done"] = True
+
+    for key, details in stages.items():
+        graph.node(key, details["label"], fillcolor=details["fillcolor"], color=details["color"], shape='box', style='filled,rounded')
+
+    graph.edge("upload", "frames")
+    graph.edge("frames", "preprocessing")
+    graph.edge("preprocessing", "model")
+    graph.edge("model", "results")
+
+    return graph
+
+def extract_faces_from_frames(video_path, num_frames=15):
+    """Extract faces from video frames and display some of them"""
+    cap = cv2.VideoCapture(video_path)
+    
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frames_to_extract = min(num_frames, total_frames)
+    interval = max(1, total_frames // frames_to_extract)
+    
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    face_frames = []
+    
+    for i in range(0, total_frames, interval):
+        if len(face_frames) >= frames_to_extract:
+            break
+            
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        ret, frame = cap.read()
+        if not ret:
+            continue
+            
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+        
+        if len(faces) > 0:
+            face_frames.append(frame)
+    
+    cap.release()
+    return face_frames[:frames_to_extract]
+
+def process_video(video_file, model, config, num_frames=15, progress_bar=None, flowchart_placeholder=None):
+    """Process a video file and return prediction"""
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file:
+        tmp_file.write(video_file.read())
+        tmp_file_path = tmp_file.name
+
+    total_steps = 4
+    progress_step = 0
+
+    try:
+        add_log(f"Processing video: {video_file.name}")
+        if flowchart_placeholder:
+            flowchart_placeholder.graphviz_chart(create_flowchart("frames"))
+
+        progress_step += 1
+        if progress_bar:
+            progress_bar.progress(progress_step / total_steps, "Extracting faces...")
+            
+        with st.spinner("Extracting faces from video frames..."):
+            df = df_face(tmp_file_path, num_frames, "genconvit")
+            add_log(f"Extracted {len(df)} face frames")
+
+        if len(df) >= 1:
+            if flowchart_placeholder:
+                flowchart_placeholder.graphviz_chart(create_flowchart("preprocessing"))
+
+            progress_step += 1
+            if progress_bar:
+                progress_bar.progress(progress_step / total_steps, "Preprocessing frames...")
+            time.sleep(0.5)
+
+            if flowchart_placeholder:
+                flowchart_placeholder.graphviz_chart(create_flowchart("model"))
+
+            progress_step += 1
+            if progress_bar:
+                progress_bar.progress(progress_step / total_steps, "Analyzing with GenConViT...")
+
+            with st.spinner("Analyzing video..."):
+                y, y_val = pred_vid(df, model)
+                prediction = real_or_fake(y)
+                confidence = float(y_val)
+                add_log(f"Prediction: {prediction} with confidence {confidence:.4f}")
+        else:
+            prediction = "Unable to detect faces"
+            confidence = 0.0
+            add_log("No faces detected in video")
+
+        if flowchart_placeholder:
+            flowchart_placeholder.graphviz_chart(create_flowchart("results"))
+        progress_step += 1
+        if progress_bar:
+            progress_bar.progress(progress_step / total_steps, "Results ready!")
+
+        os.unlink(tmp_file_path)
+        add_log("Temporary video file removed")
+        return prediction, confidence, df
+
+    except Exception as e:
+        if os.path.exists(tmp_file_path):
+            os.unlink(tmp_file_path)
+        add_log(f"Error processing video: {str(e)}")
+        st.error(f"Error processing video: {str(e)}")
+        return "Error", 0.0, None
+
+def main():
+    st.sidebar.title("GenConViT Deepfake Detector")
+    page = st.sidebar.radio("Navigation", ["Home", "About", "How It Works"])
+    
+    model_type = st.sidebar.selectbox(
+        "Select Model",
+        options=["Both (ED + VAE)", "ED Model Only", "VAE Model Only"],
+        index=0,
+        help="Choose which model components to use for detection."
+    )
+    
+    model_type_map = {
+        "Both (ED + VAE)": "both",
+        "ED Model Only": "ed",
+        "VAE Model Only": "vae"
+    }
+    selected_model_type = model_type_map[model_type]
+
+    if page == "Home":
+        st.title("🎭 Deepfake Detection with GenConViT")
+        st.markdown("""
+        Upload a video to detect if it's a real or fake (manipulated) facial video.
+        This app uses the GenConViT model to analyze facial videos for signs of manipulation.
+        """)
+
+        if 'model_loaded' not in st.session_state:
+            st.session_state.model_loaded = False
+        
+        if not st.session_state.model_loaded:
+            try:
+                with st.spinner("⏳ Loading AI model..."):
+                    model, config = load_model_from_huggingface(model_type=selected_model_type)
+                st.success("✅ Model loaded successfully")
+                st.session_state.model = model
+                st.session_state.config = config
+                st.session_state.model_loaded = True
+                st.session_state.model_type = model_type
+            except Exception as e:
+                st.error(f"Failed to load model: {str(e)}")
+                st.stop()
+        else:
+            model = st.session_state.model
+            config = st.session_state.config
+
+        uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "avi", "mov", "wmv"])
+        
+        col1, col2 = st.columns([1, 1])
+        with col1:
+            num_frames = st.slider("Number of frames to process", min_value=5, max_value=30, value=15)
+        
+
+        progress_bar_placeholder = st.empty()
+        flowchart_placeholder = st.empty()
+        
+        result_container = st.container()
+        details_container = st.container()
+        
+        if uploaded_file is not None:
+            flowchart_placeholder.graphviz_chart(create_flowchart("upload"))
+            progress_bar = progress_bar_placeholder.progress(0, "Starting analysis...")
+            st.video(uploaded_file)
+            
+            prediction, confidence, tensor_data = process_video(
+                uploaded_file, model, config, num_frames, progress_bar, flowchart_placeholder
+            )
+            
+            with result_container:
+                st.subheader("Analysis Results")
+                col1, col2 = st.columns([1, 1])
+                
+                with col1:
+                    if prediction == "FAKE":
+                        st.error("⚠️ DEEPFAKE DETECTED")
+                        st.metric("Confidence", f"{confidence:.2f}")
+                        st.markdown("This video appears to be manipulated.")
+                    elif prediction == "REAL":
+                        st.success("✅ AUTHENTIC VIDEO")
+                        st.metric("Confidence", f"{(1 - confidence):.2f}")  # Show "real" confidence
+                        st.markdown("This video appears to be authentic.")
+                    else:
+                        st.warning(f"⚠️ {prediction}")
+                
+                with col2:
+                    if prediction != "Unable to detect faces" and prediction != "Error":
+                        fake_percentage = confidence * 100
+                        real_percentage = (1 - confidence) * 100
+                        chart_data = pd.DataFrame({
+                            "Category": ["Real", "Fake"],
+                            "Percentage": [real_percentage, fake_percentage]
+                        })
+                        st.bar_chart(chart_data.set_index("Category"))
+                
+                # Add radar chart for more detailed visualization
+                if prediction != "Unable to detect faces" and prediction != "Error":
+                    st.subheader("Confidence Analysis")
+                    
+                    # Create radar chart data
+                    radar_data = {
+                        'Metrics': ['Authenticity', 'Manipulation', 'Confidence', 'Certainty', 'Reliability'],
+                        'Real': [real_percentage, 100-fake_percentage, real_percentage, 
+                                real_percentage*0.9, real_percentage*1.1],
+                        'Fake': [fake_percentage, 100-real_percentage, fake_percentage, 
+                                fake_percentage*0.9, fake_percentage*1.1]
+                    }
+                    
+                    radar_df = pd.DataFrame(radar_data)
+                    
+                    # Plot radar chart using plotly
+                    import plotly.graph_objects as go
+                    
+                    categories = radar_df['Metrics'].tolist()
+                    fig = go.Figure()
+                    
+                    fig.add_trace(go.Scatterpolar(
+                        r=radar_df['Real'].tolist(),
+                        theta=categories,
+                        fill='toself',
+                        name='Real'
+                    ))
+                    
+                    fig.add_trace(go.Scatterpolar(
+                        r=radar_df['Fake'].tolist(),
+                        theta=categories,
+                        fill='toself',
+                        name='Fake'
+                    ))
+                    
+                    fig.update_layout(
+                        polar=dict(
+                            radialaxis=dict(
+                                visible=True,
+                                range=[0, 100]
+                            )
+                        ),
+                        showlegend=True
+                    )
+                    
+                    st.plotly_chart(fig, use_container_width=True)
+            
+            with details_container:
+                st.subheader("Detailed Analysis")
+                current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                details = {
+                    "Metric": ["Video", "Model Used", "Frames Analyzed", "Result", "Confidence", "Date/Time"],
+                    "Value": [
+                        uploaded_file.name, 
+                        model_type,
+                        str(num_frames),  # Convert to string to avoid PyArrow type issues
+                        prediction, 
+                        f"{confidence:.4f}", 
+                        current_time
+                    ]
+                }
+                
+                df_details = pd.DataFrame(details)
+                st.dataframe(df_details, use_container_width=True)
+                
+                csv = df_details.to_csv(index=False)
+                st.download_button(
+                    label="📊 Export Results as CSV",
+                    data=csv,
+                    file_name=f"deepfake_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
+                    mime="text/csv",
+                )
+        
+        # Logs Section
+        with st.expander("Processing Logs", expanded=False):
+            st.subheader("Logs")
+            if st.session_state.logs:
+                log_text = "\n".join(st.session_state.logs)
+                st.text_area("Log Output", value=log_text, height=200, disabled=True)
+            else:
+                st.info("No logs available yet.")
+            if st.button("Clear Logs"):
+                st.session_state.logs = []
+                st.rerun()
+    
+    elif page == "About":
+        st.title("About GenConViT")
+        st.markdown("""
+        ## What is GenConViT?
+        
+        GenConViT is a deepfake detection model that combines convolutional neural networks with vision transformers
+        to detect manipulated facial videos with high accuracy.
+        
+        ### Key Features
+        
+        - **Robust Detection**: Trained on multiple deepfake datasets
+        - **High Accuracy**: Achieves state-of-the-art performance
+        - **Real-time Analysis**: Fast processing for quick results
+        
+        ### Capabilities
+        
+        The model can detect various types of facial manipulations including:
+        - Face swaps
+        - Face reenactment
+        - Face synthesis
+        - Attribute manipulation
+        
+        ### Model Architecture
+        """)
+        
+        st.image("pipeline_architecture.png", 
+                 caption="GenConViT Architecture Diagram")
+        
+        st.markdown("""
+        ### Citations
+        
+        If you use GenConViT in your research or applications, please cite:
+        
+        ```
+        @article{deressa2023genconvit,
+          title={GenConViT: Generalized Convolutional Vision Transformer for Deepfake Detection},
+          author={Deressa, Safal and Colleagues},
+          journal={arXiv preprint},
+          year={2023}
+        }
+        ```
+        
+        ### Source Code
+        
+        The model is available on GitHub: [https://github.com/Deressa/GenConViT](https://github.com/Deressa/GenConViT)
+        """)
+        
+    elif page == "How It Works":
+        st.title("How GenConViT Works")
+        st.markdown("""
+        ## Deepfake Detection Pipeline
+        
+        GenConViT processes videos through a series of steps to determine if they're real or fake:
+        """)
+        st.graphviz_chart(create_flowchart())
+        st.markdown("""
+        ### 1. Video Upload
+        The process begins when you upload a video file to be analyzed.
+        
+        ### 2. Frame Extraction
+        The system extracts key frames from the video for analysis.
+        
+        ### 3. Preprocessing
+        Frames are preprocessed to detect and crop faces, normalize lighting, and prepare for analysis.
+        
+        ### 4. Model Analysis
+        The GenConViT model analyzes the facial features and movement patterns to detect signs of manipulation.
+        
+        ### 5. Results
+        The system provides a prediction along with a confidence score, indicating whether the video is real or fake.
+        
+        ## Technical Details
+        
+        GenConViT combines the strengths of:
+        - Convolutional Neural Networks (CNN) for local feature extraction
+        - Vision Transformers (ViT) for global context understanding
+        
+        This hybrid approach enables better detection across different types of deepfakes and manipulation techniques.
+        """)
+
+st.sidebar.markdown("---")
+st.sidebar.markdown("© 2025 GenConViT")
+st.sidebar.markdown("Created by Safal Immanuel Sabari")
+
+if __name__ == "__main__":
+    main()

dataset/loader.py

@@ -0,0 +1,141 @@
+import os
+import torch
+from torchvision import transforms, datasets
+from albumentations import (
+    HorizontalFlip,
+    VerticalFlip,
+    ShiftScaleRotate,
+    CLAHE,
+    RandomRotate90,
+    Transpose,
+    ShiftScaleRotate,
+    HueSaturationValue,
+    GaussNoise,
+    Sharpen,
+    Emboss,
+    RandomBrightnessContrast,
+    OneOf,
+    Compose,
+)
+import numpy as np
+from PIL import Image
+
+
+def strong_aug(p=0.5):
+    return Compose(
+        [
+            RandomRotate90(p=0.2),
+            Transpose(p=0.2),
+            HorizontalFlip(p=0.5),
+            VerticalFlip(p=0.5),
+            OneOf(
+                [
+                    GaussNoise(),
+                ],
+                p=0.2,
+            ),
+            ShiftScaleRotate(p=0.2),
+            OneOf(
+                [
+                    CLAHE(clip_limit=2),
+                    Sharpen(),
+                    Emboss(),
+                    RandomBrightnessContrast(),
+                ],
+                p=0.2,
+            ),
+            HueSaturationValue(p=0.2),
+        ],
+        p=p,
+    )
+
+
+def augment(aug, image):
+    return aug(image=image)["image"]
+
+
+class Aug(object):
+    def __call__(self, img):
+        aug = strong_aug(p=0.9)
+        return Image.fromarray(augment(aug, np.array(img)))
+
+
+def normalize_data():
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+
+    return {
+        "train": transforms.Compose(
+            [Aug(), transforms.ToTensor(), transforms.Normalize(mean, std)]
+        ),
+        "valid": transforms.Compose(
+            [transforms.ToTensor(), transforms.Normalize(mean, std)]
+        ),
+        "test": transforms.Compose(
+            [transforms.ToTensor(), transforms.Normalize(mean, std)]
+        ),
+        "vid": transforms.Compose([transforms.Normalize(mean, std)]),
+    }
+
+
+def load_data(data_dir="sample/", batch_size=4):
+    data_dir = data_dir
+    image_datasets = {
+        x: datasets.ImageFolder(os.path.join(data_dir, x), normalize_data()[x])
+        for x in ["train", "valid", "test"]
+    }
+
+    # dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size,
+    #                                             shuffle=True, num_workers=0, pin_memory=True)
+    #               for x in ['train', 'validation', 'test']}
+
+    dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "valid", "test"]}
+
+    train_dataloaders = torch.utils.data.DataLoader(
+        image_datasets["train"],
+        batch_size,
+        shuffle=True,
+        num_workers=0,
+        pin_memory=True,
+    )
+    validation_dataloaders = torch.utils.data.DataLoader(
+        image_datasets["valid"],
+        batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+    )
+    test_dataloaders = torch.utils.data.DataLoader(
+        image_datasets["test"],
+        batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+    )
+
+    dataloaders = {
+        "train": train_dataloaders,
+        "validation": validation_dataloaders,
+        "test": test_dataloaders,
+    }
+
+    return dataloaders, dataset_sizes
+
+
+def load_checkpoint(model, optimizer, filename=None):
+    start_epoch = 0
+    log_loss = 0
+    if os.path.isfile(filename):
+        print("=> loading checkpoint '{}'".format(filename))
+        checkpoint = torch.load(filename)
+        start_epoch = checkpoint["epoch"]
+        model.load_state_dict(checkpoint["state_dict"])
+        optimizer.load_state_dict(checkpoint["optimizer"])
+        log_loss = checkpoint["min_loss"]
+        print(
+            "=> loaded checkpoint '{}' (epoch {})".format(filename, checkpoint["epoch"])
+        )
+    else:
+        print("=> no checkpoint found at '{}'".format(filename))
+
+    return model, optimizer, start_epoch, log_loss

json_file/celeb_test.json

@@ -0,0 +1 @@
+["YouTube-real/00170.mp4", "YouTube-real/00208.mp4", "YouTube-real/00063.mp4", "YouTube-real/00024.mp4", "YouTube-real/00021.mp4", "YouTube-real/00036.mp4", "YouTube-real/00202.mp4", "YouTube-real/00236.mp4", "YouTube-real/00197.mp4", "YouTube-real/00133.mp4", "YouTube-real/00213.mp4", "YouTube-real/00011.mp4", "YouTube-real/00095.mp4", "YouTube-real/00138.mp4", "YouTube-real/00106.mp4", "YouTube-real/00194.mp4", "YouTube-real/00092.mp4", "YouTube-real/00227.mp4", "YouTube-real/00244.mp4", "YouTube-real/00119.mp4", "YouTube-real/00082.mp4", "YouTube-real/00188.mp4", "YouTube-real/00076.mp4", "YouTube-real/00047.mp4", "YouTube-real/00168.mp4", "YouTube-real/00207.mp4", "YouTube-real/00193.mp4", "YouTube-real/00061.mp4", "YouTube-real/00023.mp4", "YouTube-real/00048.mp4", "YouTube-real/00250.mp4", "YouTube-real/00251.mp4", "YouTube-real/00252.mp4", "YouTube-real/00253.mp4", "YouTube-real/00254.mp4", "YouTube-real/00255.mp4", "YouTube-real/00256.mp4", "YouTube-real/00257.mp4", "YouTube-real/00258.mp4", "YouTube-real/00259.mp4", "YouTube-real/00260.mp4", "YouTube-real/00261.mp4", "YouTube-real/00262.mp4", "YouTube-real/00263.mp4", "YouTube-real/00264.mp4", "YouTube-real/00265.mp4", "YouTube-real/00266.mp4", "YouTube-real/00267.mp4", "YouTube-real/00268.mp4", "YouTube-real/00269.mp4", "YouTube-real/00270.mp4", "YouTube-real/00271.mp4", "YouTube-real/00272.mp4", "YouTube-real/00273.mp4", "YouTube-real/00274.mp4", "YouTube-real/00275.mp4", "YouTube-real/00276.mp4", "YouTube-real/00277.mp4", "YouTube-real/00278.mp4", "YouTube-real/00279.mp4", "YouTube-real/00280.mp4", "YouTube-real/00281.mp4", "YouTube-real/00282.mp4", "YouTube-real/00283.mp4", "YouTube-real/00284.mp4", "YouTube-real/00285.mp4", "YouTube-real/00286.mp4", "YouTube-real/00287.mp4", "YouTube-real/00288.mp4", "YouTube-real/00289.mp4", "Celeb-real/id1_0007.mp4", "Celeb-real/id2_0008.mp4", "Celeb-real/id3_0001.mp4", "Celeb-real/id6_0005.mp4", "Celeb-real/id9_0000.mp4", "Celeb-real/id11_0008.mp4", "Celeb-real/id16_0011.mp4", "Celeb-real/id17_0000.mp4", "Celeb-real/id35_0008.mp4", "Celeb-real/id39_0005.mp4", "Celeb-real/id48_0000.mp4", "Celeb-real/id32_0008.mp4", "Celeb-real/id56_0009.mp4", "Celeb-real/id54_0007.mp4", "Celeb-real/id37_0005.mp4", "Celeb-real/id29_0008.mp4", "Celeb-real/id43_0006.mp4", "Celeb-real/id1_0006.mp4", "Celeb-real/id51_0001.mp4", "Celeb-real/id31_0007.mp4", "Celeb-real/id2_0003.mp4", "Celeb-real/id21_0009.mp4", "Celeb-real/id50_0003.mp4", "Celeb-real/id21_0006.mp4", "Celeb-real/id44_0003.mp4", "Celeb-real/id36_0006.mp4", "Celeb-real/id36_0008.mp4", "Celeb-real/id1_0008.mp4", "Celeb-real/id45_0003.mp4", "Celeb-real/id48_0003.mp4", "Celeb-real/id23_0001.mp4", "Celeb-real/id16_0010.mp4", "Celeb-real/id31_0004.mp4", "Celeb-real/id35_0000.mp4", "Celeb-real/id58_0002.mp4", "Celeb-real/id30_0007.mp4", "Celeb-real/id12_0005.mp4", "Celeb-real/id56_0002.mp4", "Celeb-real/id33_0005.mp4", "Celeb-real/id29_0009.mp4", "Celeb-real/id13_0013.mp4", "Celeb-real/id28_0009.mp4", "Celeb-real/id36_0000.mp4", "Celeb-real/id24_0009.mp4", "Celeb-real/id39_0004.mp4", "Celeb-real/id27_0008.mp4", "Celeb-real/id11_0006.mp4", "Celeb-real/id19_0007.mp4", "Celeb-real/id37_0009.mp4", "Celeb-real/id3_0008.mp4", "Celeb-real/id46_0000.mp4", "Celeb-real/id33_0008.mp4", "Celeb-real/id8_0000.mp4", "Celeb-real/id16_0007.mp4", "Celeb-real/id1_0001.mp4", "Celeb-real/id32_0002.mp4", "Celeb-real/id19_0005.mp4", "Celeb-real/id27_0009.mp4", "Celeb-real/id2_0001.mp4", "Celeb-real/id13_0011.mp4", "Celeb-real/id49_0004.mp4", "Celeb-real/id57_0007.mp4", "Celeb-real/id26_0008.mp4", "Celeb-real/id2_0004.mp4", "Celeb-real/id36_0001.mp4", "Celeb-real/id0_0001.mp4", "Celeb-real/id10_0007.mp4", "Celeb-real/id17_0002.mp4", "Celeb-real/id4_0004.mp4", "Celeb-real/id36_0002.mp4", "Celeb-real/id53_0003.mp4", "Celeb-real/id25_0010.mp4", "Celeb-real/id51_0006.mp4", "Celeb-real/id22_0000.mp4", "Celeb-real/id10_0001.mp4", "Celeb-real/id6_0002.mp4", "Celeb-real/id12_0000.mp4", "Celeb-real/id35_0003.mp4", "Celeb-real/id3_0002.mp4", "Celeb-real/id31_0003.mp4", "Celeb-real/id52_0002.mp4", "Celeb-real/id2_0002.mp4", "Celeb-real/id44_0001.mp4", "Celeb-real/id8_0009.mp4", "Celeb-real/id33_0002.mp4", "Celeb-real/id50_0000.mp4", "Celeb-real/id40_0003.mp4", "Celeb-real/id7_0006.mp4", "Celeb-real/id53_0007.mp4", "Celeb-real/id43_0002.mp4", "Celeb-real/id56_0000.mp4", "Celeb-real/id13_0004.mp4", "Celeb-real/id43_0001.mp4", "Celeb-real/id20_0008.mp4", "Celeb-real/id28_0004.mp4", "Celeb-real/id22_0009.mp4", "Celeb-real/id53_0000.mp4", "Celeb-real/id50_0006.mp4", "Celeb-real/id32_0003.mp4", "Celeb-real/id32_0006.mp4", "Celeb-real/id57_0003.mp4", "Celeb-real/id37_0004.mp4", "Celeb-real/id28_0007.mp4", "Celeb-real/id52_0001.mp4", "Celeb-real/id40_0009.mp4", "Celeb-real/id51_0005.mp4", "Celeb-real/id34_0006.mp4", "Celeb-real/id42_0002.mp4", "Celeb-synthesis/id1_id0_0007.mp4", "Celeb-synthesis/id1_id3_0007.mp4", "Celeb-synthesis/id1_id4_0007.mp4", "Celeb-synthesis/id1_id9_0007.mp4", "Celeb-synthesis/id1_id16_0007.mp4", "Celeb-synthesis/id1_id17_0007.mp4", "Celeb-synthesis/id2_id0_0008.mp4", "Celeb-synthesis/id2_id3_0008.mp4", "Celeb-synthesis/id2_id9_0008.mp4", "Celeb-synthesis/id3_id1_0001.mp4", "Celeb-synthesis/id3_id2_0001.mp4", "Celeb-synthesis/id3_id4_0001.mp4", "Celeb-synthesis/id3_id6_0001.mp4", "Celeb-synthesis/id3_id9_0001.mp4", "Celeb-synthesis/id3_id16_0001.mp4", "Celeb-synthesis/id4_id1_0003.mp4", "Celeb-synthesis/id6_id0_0005.mp4", "Celeb-synthesis/id6_id1_0005.mp4", "Celeb-synthesis/id6_id2_0005.mp4", "Celeb-synthesis/id6_id3_0005.mp4", "Celeb-synthesis/id6_id16_0005.mp4", "Celeb-synthesis/id7_id10_0009.mp4", "Celeb-synthesis/id7_id11_0009.mp4", "Celeb-synthesis/id7_id13_0009.mp4", "Celeb-synthesis/id9_id0_0000.mp4", "Celeb-synthesis/id9_id1_0000.mp4", "Celeb-synthesis/id9_id2_0000.mp4", "Celeb-synthesis/id9_id3_0000.mp4", "Celeb-synthesis/id9_id6_0000.mp4", "Celeb-synthesis/id9_id16_0000.mp4", "Celeb-synthesis/id10_id7_0004.mp4", "Celeb-synthesis/id10_id11_0004.mp4", "Celeb-synthesis/id10_id12_0004.mp4", "Celeb-synthesis/id10_id13_0004.mp4", "Celeb-synthesis/id10_id7_0001.mp4", "Celeb-synthesis/id10_id11_0001.mp4", "Celeb-synthesis/id10_id12_0001.mp4", "Celeb-synthesis/id10_id13_0001.mp4", "Celeb-synthesis/id11_id7_0008.mp4", "Celeb-synthesis/id16_id0_0011.mp4", "Celeb-synthesis/id16_id1_0011.mp4", "Celeb-synthesis/id16_id2_0011.mp4", "Celeb-synthesis/id16_id3_0011.mp4", "Celeb-synthesis/id16_id6_0011.mp4", "Celeb-synthesis/id17_id0_0000.mp4", "Celeb-synthesis/id17_id1_0000.mp4", "Celeb-synthesis/id17_id2_0000.mp4", "Celeb-synthesis/id17_id3_0000.mp4", "Celeb-synthesis/id17_id6_0000.mp4", "Celeb-synthesis/id17_id9_0000.mp4", "Celeb-synthesis/id17_id16_0000.mp4", "Celeb-synthesis/id16_id20_0000.mp4", "Celeb-synthesis/id17_id23_0000.mp4", "Celeb-synthesis/id3_id28_0001.mp4", "Celeb-synthesis/id35_id32_0007.mp4", "Celeb-synthesis/id30_id26_0004.mp4", "Celeb-synthesis/id16_id20_0006.mp4", "Celeb-synthesis/id4_id20_0001.mp4", "Celeb-synthesis/id26_id3_0002.mp4", "Celeb-synthesis/id20_id31_0006.mp4", "Celeb-synthesis/id6_id23_0001.mp4", "Celeb-synthesis/id9_id23_0009.mp4", "Celeb-synthesis/id32_id33_0002.mp4", "Celeb-synthesis/id16_id20_0012.mp4", "Celeb-synthesis/id29_id30_0000.mp4", "Celeb-synthesis/id3_id20_0008.mp4", "Celeb-synthesis/id22_id27_0001.mp4", "Celeb-synthesis/id27_id21_0002.mp4", "Celeb-synthesis/id29_id35_0000.mp4", "Celeb-synthesis/id21_id19_0005.mp4", "Celeb-synthesis/id23_id26_0003.mp4", "Celeb-synthesis/id30_id35_0009.mp4", "Celeb-synthesis/id9_id26_0000.mp4", "Celeb-synthesis/id33_id32_0006.mp4", "Celeb-synthesis/id30_id34_0003.mp4", "Celeb-synthesis/id29_id37_0008.mp4", "Celeb-synthesis/id35_id31_0006.mp4", "Celeb-synthesis/id37_id34_0008.mp4", "Celeb-synthesis/id26_id16_0001.mp4", "Celeb-synthesis/id17_id23_0009.mp4", "Celeb-synthesis/id6_id20_0007.mp4", "Celeb-synthesis/id1_id20_0001.mp4", "Celeb-synthesis/id1_id23_0006.mp4", "Celeb-synthesis/id37_id35_0001.mp4", "Celeb-synthesis/id4_id23_0006.mp4", "Celeb-synthesis/id17_id26_0006.mp4", "Celeb-synthesis/id20_id1_0007.mp4", "Celeb-synthesis/id0_id26_0009.mp4", "Celeb-synthesis/id38_id33_0005.mp4", "Celeb-synthesis/id28_id35_0008.mp4", "Celeb-synthesis/id23_id35_0006.mp4", "Celeb-synthesis/id6_id28_0002.mp4", "Celeb-synthesis/id35_id28_0006.mp4", "Celeb-synthesis/id32_id31_0008.mp4", "Celeb-synthesis/id38_id34_0004.mp4", "Celeb-synthesis/id3_id28_0009.mp4", "Celeb-synthesis/id37_id28_0007.mp4", "Celeb-synthesis/id1_id20_0002.mp4", "Celeb-synthesis/id37_id31_0008.mp4", "Celeb-synthesis/id30_id23_0007.mp4", "Celeb-synthesis/id28_id26_0000.mp4", "Celeb-synthesis/id2_id28_0003.mp4", "Celeb-synthesis/id27_id24_0007.mp4", "Celeb-synthesis/id30_id32_0006.mp4", "Celeb-synthesis/id35_id31_0009.mp4", "Celeb-synthesis/id4_id21_0004.mp4", "Celeb-synthesis/id37_id21_0005.mp4", "Celeb-synthesis/id30_id21_0002.mp4", "Celeb-synthesis/id16_id23_0011.mp4", "Celeb-synthesis/id9_id20_0002.mp4", "Celeb-synthesis/id6_id21_0006.mp4", "Celeb-synthesis/id20_id19_0002.mp4", "Celeb-synthesis/id28_id4_0006.mp4", "Celeb-synthesis/id35_id34_0002.mp4", "Celeb-synthesis/id35_id26_0003.mp4", "Celeb-synthesis/id22_id28_0006.mp4", "Celeb-synthesis/id30_id21_0005.mp4", "Celeb-synthesis/id24_id20_0009.mp4", "Celeb-synthesis/id4_id20_0000.mp4", "Celeb-synthesis/id2_id26_0001.mp4", "Celeb-synthesis/id21_id6_0005.mp4", "Celeb-synthesis/id21_id9_0008.mp4", "Celeb-synthesis/id22_id21_0005.mp4", "Celeb-synthesis/id22_id27_0005.mp4", "Celeb-synthesis/id23_id2_0007.mp4", "Celeb-synthesis/id23_id3_0009.mp4", "Celeb-synthesis/id24_id19_0000.mp4", "Celeb-synthesis/id24_id21_0006.mp4", "Celeb-synthesis/id24_id26_0009.mp4", "Celeb-synthesis/id25_id19_0002.mp4", "Celeb-synthesis/id25_id24_0003.mp4", "Celeb-synthesis/id27_id21_0008.mp4", "Celeb-synthesis/id27_id22_0007.mp4", "Celeb-synthesis/id27_id25_0008.mp4", "Celeb-synthesis/id28_id3_0004.mp4", "Celeb-synthesis/id28_id6_0006.mp4", "Celeb-synthesis/id29_id32_0000.mp4", "Celeb-synthesis/id29_id37_0009.mp4", "Celeb-synthesis/id30_id20_0004.mp4", "Celeb-synthesis/id30_id23_0002.mp4", "Celeb-synthesis/id32_id33_0004.mp4", "Celeb-synthesis/id33_id38_0001.mp4", "Celeb-synthesis/id34_id33_0005.mp4", "Celeb-synthesis/id35_id31_0007.mp4", "Celeb-synthesis/id49_id52_0005.mp4", "Celeb-synthesis/id9_id2_0006.mp4", "Celeb-synthesis/id7_id11_0007.mp4", "Celeb-synthesis/id4_id37_0006.mp4", "Celeb-synthesis/id37_id38_0009.mp4", "Celeb-synthesis/id29_id30_0009.mp4", "Celeb-synthesis/id0_id6_0007.mp4", "Celeb-synthesis/id0_id16_0004.mp4", "Celeb-synthesis/id31_id33_0008.mp4", "Celeb-synthesis/id54_id49_0003.mp4", "Celeb-synthesis/id0_id1_0000.mp4", "Celeb-synthesis/id21_id28_0001.mp4", "Celeb-synthesis/id33_id29_0004.mp4", "Celeb-synthesis/id28_id0_0009.mp4", "Celeb-synthesis/id6_id9_0006.mp4", "Celeb-synthesis/id9_id0_0006.mp4", "Celeb-synthesis/id26_id2_0005.mp4", "Celeb-synthesis/id1_id3_0002.mp4", "Celeb-synthesis/id4_id6_0008.mp4", "Celeb-synthesis/id16_id28_0005.mp4", "Celeb-synthesis/id37_id35_0005.mp4", "Celeb-synthesis/id19_id20_0004.mp4", "Celeb-synthesis/id46_id41_0000.mp4", "Celeb-synthesis/id51_id57_0004.mp4", "Celeb-synthesis/id29_id37_0005.mp4", "Celeb-synthesis/id35_id23_0007.mp4", "Celeb-synthesis/id34_id33_0002.mp4", "Celeb-synthesis/id1_id3_0006.mp4", "Celeb-synthesis/id13_id10_0005.mp4", "Celeb-synthesis/id56_id55_0009.mp4", "Celeb-synthesis/id25_id28_0010.mp4", "Celeb-synthesis/id20_id37_0004.mp4", "Celeb-synthesis/id37_id29_0002.mp4", "Celeb-synthesis/id56_id54_0007.mp4", "Celeb-synthesis/id19_id20_0000.mp4", "Celeb-synthesis/id4_id28_0006.mp4", "Celeb-synthesis/id53_id58_0005.mp4", "Celeb-synthesis/id55_id57_0002.mp4", "Celeb-synthesis/id24_id26_0004.mp4", "Celeb-synthesis/id20_id0_0006.mp4", "Celeb-synthesis/id37_id2_0009.mp4", "Celeb-synthesis/id30_id26_0006.mp4", "Celeb-synthesis/id16_id2_0001.mp4", "Celeb-synthesis/id26_id1_0002.mp4", "Celeb-synthesis/id30_id6_0007.mp4", "Celeb-synthesis/id44_id41_0005.mp4", "Celeb-synthesis/id16_id2_0006.mp4", "Celeb-synthesis/id1_id16_0004.mp4", "Celeb-synthesis/id16_id21_0002.mp4", "Celeb-synthesis/id4_id0_0000.mp4", "Celeb-synthesis/id8_id0_0002.mp4", "Celeb-synthesis/id19_id23_0001.mp4", "Celeb-synthesis/id37_id3_0006.mp4", "Celeb-synthesis/id9_id16_0007.mp4", "Celeb-synthesis/id47_id43_0007.mp4", "Celeb-synthesis/id16_id3_0009.mp4", "Celeb-synthesis/id26_id28_0002.mp4", "Celeb-synthesis/id8_id3_0003.mp4", "Celeb-synthesis/id30_id28_0000.mp4", "Celeb-synthesis/id27_id23_0008.mp4", "Celeb-synthesis/id28_id20_0008.mp4", "Celeb-synthesis/id1_id2_0002.mp4", "Celeb-synthesis/id46_id39_0000.mp4", "Celeb-synthesis/id37_id28_0008.mp4", "Celeb-synthesis/id0_id3_0007.mp4", "Celeb-synthesis/id4_id1_0002.mp4", "Celeb-synthesis/id13_id7_0000.mp4", "Celeb-synthesis/id50_id53_0000.mp4", 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json_file/ff_file_list.json

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"02__outside_talking_still_laughing_actors_c23.mp4", "02__podium_speech_happy_actors_c23.mp4", "02__secret_conversation_actors_c23.mp4", "02__talking_against_wall_actors_c23.mp4", "02__talking_angry_couch_actors_c23.mp4", "02__walking_and_outside_surprised_actors_c23.mp4", "02__walking_down_indoor_hall_disgust_actors_c23.mp4", "02__walking_down_street_outside_angry_actors_c23.mp4", "02__walking_outside_cafe_disgusted_actors_c23.mp4", "02__walk_down_hall_angry_actors_c23.mp4", "03__exit_phone_room_actors_c23.mp4", "03__hugging_happy_actors_c23.mp4", "03__kitchen_pan_actors_c23.mp4", "03__kitchen_still_actors_c23.mp4", "03__meeting_serious_actors_c23.mp4", "03__outside_talking_pan_laughing_actors_c23.mp4", "03__outside_talking_still_laughing_actors_c23.mp4", "03__podium_speech_happy_actors_c23.mp4", "03__secret_conversation_actors_c23.mp4", "03__talking_against_wall_actors_c23.mp4", "03__talking_angry_couch_actors_c23.mp4", "03__walking_and_outside_surprised_actors_c23.mp4", "03__walking_down_indoor_hall_disgust_actors_c23.mp4", "03__walking_down_street_outside_angry_actors_c23.mp4", "03__walking_outside_cafe_disgusted_actors_c23.mp4", "03__walk_down_hall_angry_actors_c23.mp4", "04__exit_phone_room_actors_c23.mp4", "04__kitchen_pan_actors_c23.mp4", "04__kitchen_still_actors_c23.mp4", "04__outside_talking_pan_laughing_actors_c23.mp4", "04__outside_talking_still_laughing_actors_c23.mp4", "04__podium_speech_happy_actors_c23.mp4", "04__secret_conversation_actors_c23.mp4", "04__talking_against_wall_actors_c23.mp4", "04__talking_angry_couch_actors_c23.mp4", "04__walking_down_street_outside_angry_actors_c23.mp4", "04__walking_outside_cafe_disgusted_actors_c23.mp4", "04__walk_down_hall_angry_actors_c23.mp4", "05__exit_phone_room_actors_c23.mp4", "05__hugging_happy_actors_c23.mp4", "05__kitchen_pan_actors_c23.mp4", "05__kitchen_still_actors_c23.mp4", "05__outside_talking_pan_laughing_actors_c23.mp4", "05__outside_talking_still_laughing_actors_c23.mp4", "05__podium_speech_happy_actors_c23.mp4", "05__talking_against_wall_actors_c23.mp4", "05__walking_down_street_outside_angry_actors_c23.mp4", "05__walking_outside_cafe_disgusted_actors_c23.mp4", "05__walk_down_hall_angry_actors_c23.mp4", "06__exit_phone_room_actors_c23.mp4", "06__hugging_happy_actors_c23.mp4", "06__kitchen_pan_actors_c23.mp4", "06__kitchen_still_actors_c23.mp4", "06__outside_talking_pan_laughing_actors_c23.mp4", "06__outside_talking_still_laughing_actors_c23.mp4", "06__podium_speech_happy_actors_c23.mp4", "06__talking_against_wall_actors_c23.mp4", "06__talking_angry_couch_actors_c23.mp4", "06__walking_and_outside_surprised_actors_c23.mp4", "06__walking_down_indoor_hall_disgust_actors_c23.mp4", "06__walking_down_street_outside_angry_actors_c23.mp4", "06__walking_outside_cafe_disgusted_actors_c23.mp4", "06__walk_down_hall_angry_actors_c23.mp4", "07__exit_phone_room_actors_c23.mp4", "07__hugging_happy_actors_c23.mp4", "07__kitchen_pan_actors_c23.mp4", "07__kitchen_still_actors_c23.mp4", "07__outside_talking_pan_laughing_actors_c23.mp4", "07__outside_talking_still_laughing_actors_c23.mp4", "07__podium_speech_happy_actors_c23.mp4", "07__secret_conversation_actors_c23.mp4", "07__talking_against_wall_actors_c23.mp4", "07__talking_angry_couch_actors_c23.mp4", "07__walking_down_street_outside_angry_actors_c23.mp4", "07__walking_outside_cafe_disgusted_actors_c23.mp4", "07__walk_down_hall_angry_actors_c23.mp4", "08__exit_phone_room_actors_c23.mp4", "08__kitchen_pan_actors_c23.mp4", "08__kitchen_still_actors_c23.mp4", "08__outside_talking_pan_laughing_actors_c23.mp4", "08__outside_talking_still_laughing_actors_c23.mp4", "08__podium_speech_happy_actors_c23.mp4", "08__talking_against_wall_actors_c23.mp4", "08__walking_down_street_outside_angry_actors_c23.mp4", "08__walking_outside_cafe_disgusted_actors_c23.mp4", "08__walk_down_hall_angry_actors_c23.mp4", "09__exit_phone_room_actors_c23.mp4", "09__kitchen_pan_actors_c23.mp4", "09__outside_talking_pan_laughing_actors_c23.mp4", "09__outside_talking_still_laughing_actors_c23.mp4", "09__podium_speech_happy_actors_c23.mp4", "09__talking_against_wall_actors_c23.mp4", "09__talking_angry_couch_actors_c23.mp4", "09__walking_down_street_outside_angry_actors_c23.mp4", "09__walk_down_hall_angry_actors_c23.mp4", "10__exit_phone_room_actors_c23.mp4", "10__kitchen_pan_actors_c23.mp4", "10__kitchen_still_actors_c23.mp4", "10__outside_talking_pan_laughing_actors_c23.mp4", "10__outside_talking_still_laughing_actors_c23.mp4", "10__podium_speech_happy_actors_c23.mp4", "10__talking_against_wall_actors_c23.mp4", "10__talking_angry_couch_actors_c23.mp4", "10__walking_down_street_outside_angry_actors_c23.mp4", "10__walking_outside_cafe_disgusted_actors_c23.mp4", "10__walk_down_hall_angry_actors_c23.mp4", "11__exit_phone_room_actors_c23.mp4", "11__kitchen_pan_actors_c23.mp4", "11__kitchen_still_actors_c23.mp4", "11__outside_talking_pan_laughing_actors_c23.mp4", "11__outside_talking_still_laughing_actors_c23.mp4", "11__podium_speech_happy_actors_c23.mp4", "11__secret_conversation_actors_c23.mp4", "11__talking_against_wall_actors_c23.mp4", "11__talking_angry_couch_actors_c23.mp4", "11__walking_down_street_outside_angry_actors_c23.mp4", "11__walking_outside_cafe_disgusted_actors_c23.mp4", "11__walk_down_hall_angry_actors_c23.mp4", "12__exit_phone_room_actors_c23.mp4", "12__hugging_happy_actors_c23.mp4", "12__kitchen_pan_actors_c23.mp4", "12__kitchen_still_actors_c23.mp4", "12__outside_talking_pan_laughing_actors_c23.mp4", "12__outside_talking_still_laughing_actors_c23.mp4", "12__podium_speech_happy_actors_c23.mp4", "12__secret_conversation_actors_c23.mp4", "12__talking_against_wall_actors_c23.mp4", "12__talking_angry_couch_actors_c23.mp4", "12__walking_and_outside_surprised_actors_c23.mp4", "12__walking_down_indoor_hall_disgust_actors_c23.mp4", "12__walking_down_street_outside_angry_actors_c23.mp4", "12__walking_outside_cafe_disgusted_actors_c23.mp4", "12__walk_down_hall_angry_actors_c23.mp4", "13__exit_phone_room_actors_c23.mp4", "13__hugging_happy_actors_c23.mp4", "13__kitchen_pan_actors_c23.mp4", "13__kitchen_still_actors_c23.mp4", "13__outside_talking_pan_laughing_actors_c23.mp4", "13__outside_talking_still_laughing_actors_c23.mp4", "13__podium_speech_happy_actors_c23.mp4", "13__secret_conversation_actors_c23.mp4", "13__talking_against_wall_actors_c23.mp4", "13__talking_angry_couch_actors_c23.mp4", "13__walking_and_outside_surprised_actors_c23.mp4", "13__walking_down_indoor_hall_disgust_actors_c23.mp4", "13__walking_down_street_outside_angry_actors_c23.mp4", "13__walking_outside_cafe_disgusted_actors_c23.mp4", "13__walk_down_hall_angry_actors_c23.mp4", "14__exit_phone_room_actors_c23.mp4", "14__hugging_happy_actors_c23.mp4", "14__kitchen_pan_actors_c23.mp4", "14__kitchen_still_actors_c23.mp4", "14__outside_talking_pan_laughing_actors_c23.mp4", "14__outside_talking_still_laughing_actors_c23.mp4", "14__podium_speech_happy_actors_c23.mp4", "14__secret_conversation_actors_c23.mp4", "14__talking_against_wall_actors_c23.mp4", "14__talking_angry_couch_actors_c23.mp4", "14__walking_and_outside_surprised_actors_c23.mp4", "14__walking_down_indoor_hall_disgust_actors_c23.mp4", "14__walking_down_street_outside_angry_actors_c23.mp4", "14__walking_outside_cafe_disgusted_actors_c23.mp4", "14__walk_down_hall_angry_actors_c23.mp4", "15__exit_phone_room_actors_c23.mp4", "15__hugging_happy_actors_c23.mp4", "15__kitchen_pan_actors_c23.mp4", "15__kitchen_still_actors_c23.mp4", "15__outside_talking_pan_laughing_actors_c23.mp4", "15__outside_talking_still_laughing_actors_c23.mp4", "15__podium_speech_happy_actors_c23.mp4", "15__talking_against_wall_actors_c23.mp4", "15__talking_angry_couch_actors_c23.mp4", "15__walking_and_outside_surprised_actors_c23.mp4", "15__walking_down_indoor_hall_disgust_actors_c23.mp4", "15__walking_down_street_outside_angry_actors_c23.mp4", "15__walking_outside_cafe_disgusted_actors_c23.mp4", "15__walk_down_hall_angry_actors_c23.mp4", "16__exit_phone_room_actors_c23.mp4", "16__hugging_happy_actors_c23.mp4", "16__kitchen_pan_actors_c23.mp4", "16__kitchen_still_actors_c23.mp4", "16__outside_talking_pan_laughing_actors_c23.mp4", "950_836_neuralTextures.mp4", "951_947_neuralTextures.mp4", "952_882_neuralTextures.mp4", "953_974_neuralTextures.mp4", "954_976_neuralTextures.mp4", "955_078_neuralTextures.mp4", "956_958_neuralTextures.mp4", "957_959_neuralTextures.mp4", "958_956_neuralTextures.mp4", "959_957_neuralTextures.mp4", "960_999_neuralTextures.mp4", "961_069_neuralTextures.mp4", "962_929_neuralTextures.mp4", "963_879_neuralTextures.mp4", "964_174_neuralTextures.mp4", "965_948_neuralTextures.mp4", "966_988_neuralTextures.mp4", "967_984_neuralTextures.mp4", "968_884_neuralTextures.mp4", "969_897_neuralTextures.mp4", "970_973_neuralTextures.mp4", "971_564_neuralTextures.mp4", "972_718_neuralTextures.mp4", "973_970_neuralTextures.mp4", "974_953_neuralTextures.mp4", "975_978_neuralTextures.mp4", "976_954_neuralTextures.mp4", "977_075_neuralTextures.mp4", "978_975_neuralTextures.mp4", "979_875_neuralTextures.mp4", "980_992_neuralTextures.mp4", "981_985_neuralTextures.mp4", "982_004_neuralTextures.mp4", "983_113_neuralTextures.mp4", "984_967_neuralTextures.mp4", "985_981_neuralTextures.mp4", "986_994_neuralTextures.mp4", "987_938_neuralTextures.mp4", "988_966_neuralTextures.mp4", "989_993_neuralTextures.mp4", "990_008_neuralTextures.mp4", "991_064_neuralTextures.mp4", "992_980_neuralTextures.mp4", "993_989_neuralTextures.mp4", "994_986_neuralTextures.mp4", "995_233_neuralTextures.mp4", "996_056_neuralTextures.mp4", "997_040_neuralTextures.mp4", "998_561_neuralTextures.mp4", "999_960_neuralTextures.mp4", "950_836_face2Face.mp4", "951_947_face2Face.mp4", "952_882_face2Face.mp4", "953_974_face2Face.mp4", "954_976_face2Face.mp4", "955_078_face2Face.mp4", "956_958_face2Face.mp4", "957_959_face2Face.mp4", "958_956_face2Face.mp4", "959_957_face2Face.mp4", "960_999_face2Face.mp4", "961_069_face2Face.mp4", "962_929_face2Face.mp4", "963_879_face2Face.mp4", "964_174_face2Face.mp4", "965_948_face2Face.mp4", "966_988_face2Face.mp4", "967_984_face2Face.mp4", "968_884_face2Face.mp4", "969_897_face2Face.mp4", "970_973_face2Face.mp4", "971_564_face2Face.mp4", "972_718_face2Face.mp4", "973_970_face2Face.mp4", "974_953_face2Face.mp4", "975_978_face2Face.mp4", "976_954_face2Face.mp4", "977_075_face2Face.mp4", "978_975_face2Face.mp4", "979_875_face2Face.mp4", "980_992_face2Face.mp4", "981_985_face2Face.mp4", "982_004_face2Face.mp4", "983_113_face2Face.mp4", "984_967_face2Face.mp4", "985_981_face2Face.mp4", "986_994_face2Face.mp4", "987_938_face2Face.mp4", "988_966_face2Face.mp4", "989_993_face2Face.mp4", "990_008_face2Face.mp4", "991_064_face2Face.mp4", "992_980_face2Face.mp4", "993_989_face2Face.mp4", "994_986_face2Face.mp4", "995_233_face2Face.mp4", "996_056_face2Face.mp4", "997_040_face2Face.mp4", "998_561_face2Face.mp4", "999_960_face2Face.mp4", "249_280c23_deepfakes.mp4", "296_293c23_deepfakes.mp4", "513_305c23_deepfakes.mp4", "950_836c23_deepfakes.mp4", "951_947c23_deepfakes.mp4", "952_882c23_deepfakes.mp4", "953_974c23_deepfakes.mp4", "954_976c23_deepfakes.mp4", "955_078c23_deepfakes.mp4", "956_958c23_deepfakes.mp4", "957_959c23_deepfakes.mp4", "958_956c23_deepfakes.mp4", "959_957c23_deepfakes.mp4", "960_999c23_deepfakes.mp4", "961_069c23_deepfakes.mp4", "962_929c23_deepfakes.mp4", "963_879c23_deepfakes.mp4", "964_174c23_deepfakes.mp4", "965_948c23_deepfakes.mp4", "966_988c23_deepfakes.mp4", "967_984c23_deepfakes.mp4", "968_884c23_deepfakes.mp4", "969_897c23_deepfakes.mp4", "970_973c23_deepfakes.mp4", "971_564c23_deepfakes.mp4", "972_718c23_deepfakes.mp4", "973_970c23_deepfakes.mp4", "974_953c23_deepfakes.mp4", "975_978c23_deepfakes.mp4", "976_954c23_deepfakes.mp4", "977_075c23_deepfakes.mp4", "978_975c23_deepfakes.mp4", "979_875c23_deepfakes.mp4", "980_992c23_deepfakes.mp4", "981_985c23_deepfakes.mp4", "982_004c23_deepfakes.mp4", "983_113c23_deepfakes.mp4", "984_967c23_deepfakes.mp4", "985_981c23_deepfakes.mp4", "986_994c23_deepfakes.mp4", "987_938c23_deepfakes.mp4", "988_966c23_deepfakes.mp4", "989_993c23_deepfakes.mp4", "990_008c23_deepfakes.mp4", "991_064c23_deepfakes.mp4", "992_980c23_deepfakes.mp4", "993_989c23_deepfakes.mp4", "994_986c23_deepfakes.mp4", "995_233c23_deepfakes.mp4", "996_056c23_deepfakes.mp4", "997_040c23_deepfakes.mp4", "998_561c23_deepfakes.mp4", "999_960c23_deepfakes.mp4", "950_836c40_Deepfakes.mp4", "951_947c40_Deepfakes.mp4", "952_882c40_Deepfakes.mp4", "953_974c40_Deepfakes.mp4", "954_976c40_Deepfakes.mp4", "955_078c40_Deepfakes.mp4", "956_958c40_Deepfakes.mp4", "957_959c40_Deepfakes.mp4", "958_956c40_Deepfakes.mp4", "959_957c40_Deepfakes.mp4", "960_999c40_Deepfakes.mp4", "961_069c40_Deepfakes.mp4", "962_929c40_Deepfakes.mp4", "963_879c40_Deepfakes.mp4", "964_174c40_Deepfakes.mp4", "965_948c40_Deepfakes.mp4", "966_988c40_Deepfakes.mp4", "967_984c40_Deepfakes.mp4", "968_884c40_Deepfakes.mp4", "969_897c40_Deepfakes.mp4", "970_973c40_Deepfakes.mp4", "971_564c40_Deepfakes.mp4", "972_718c40_Deepfakes.mp4", "973_970c40_Deepfakes.mp4", "974_953c40_Deepfakes.mp4", "975_978c40_Deepfakes.mp4", "976_954c40_Deepfakes.mp4", "977_075c40_Deepfakes.mp4", "978_975c40_Deepfakes.mp4", "979_875c40_Deepfakes.mp4", "980_992c40_Deepfakes.mp4", "981_985c40_Deepfakes.mp4", "982_004c40_Deepfakes.mp4", "983_113c40_Deepfakes.mp4", "984_967c40_Deepfakes.mp4", "985_981c40_Deepfakes.mp4", "986_994c40_Deepfakes.mp4", "987_938c40_Deepfakes.mp4", "988_966c40_Deepfakes.mp4", "989_993c40_Deepfakes.mp4", "990_008c40_Deepfakes.mp4", "991_064c40_Deepfakes.mp4", "992_980c40_Deepfakes.mp4", "993_989c40_Deepfakes.mp4", "994_986c40_Deepfakes.mp4", "995_233c40_Deepfakes.mp4", "996_056c40_Deepfakes.mp4", "997_040c40_Deepfakes.mp4", "998_561c40_Deepfakes.mp4", "999_960c40_Deepfakes.mp4"]

model/config.py

@@ -0,0 +1,10 @@
+import yaml
+import os
+
+#read yaml file
+
+def load_config():
+  with open(os.path.join('model','config.yaml')) as file:
+    config= yaml.safe_load(file)
+
+  return config

model/config.yaml

@@ -0,0 +1,12 @@
+model:
+  backbone: convnext_tiny
+  embedder: swin_tiny_patch4_window7_224
+  latent_dims: 12544
+
+batch_size: 32
+epoch: 1
+learning_rate: 0.0001
+weight_decay: 0.0001
+num_classes: 2
+img_size: 224
+min_val_loss: 10000

model/face_detection.py

@@ -0,0 +1,51 @@
+import cv2
+import numpy as np
+from tqdm import tqdm
+
+def detect_faces(frames, min_face_size=30):
+    """
+    Detect faces in frames using OpenCV's Haar Cascade classifier instead of dlib
+    
+    Args:
+        frames: List of frames to detect faces in
+        min_face_size: Minimum face size to detect
+        
+    Returns:
+        Tuple of (face_frames, count) where face_frames is a numpy array of detected faces
+        and count is the number of faces detected
+    """
+    # Initialize face detector
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    
+    # Initialize array to store faces
+    temp_face = np.zeros((len(frames), 224, 224, 3), dtype=np.uint8)
+    count = 0
+    
+    # Process each frame
+    for _, frame in tqdm(enumerate(frames), total=len(frames)):
+        # Convert to grayscale for face detection
+        gray = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+        gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
+        
+        # Detect faces
+        faces = face_cascade.detectMultiScale(
+            gray,
+            scaleFactor=1.1,
+            minNeighbors=5,
+            minSize=(min_face_size, min_face_size)
+        )
+        
+        # Process each detected face
+        for (x, y, w, h) in faces:
+            if count < len(frames):
+                # Extract and resize face
+                face_image = frame[y:y+h, x:x+w]
+                face_image = cv2.resize(face_image, (224, 224), interpolation=cv2.INTER_AREA)
+                
+                # Store face
+                temp_face[count] = face_image
+                count += 1
+            else:
+                break
+    
+    return ([], 0) if count == 0 else (temp_face[:count], count)

model/genconvit.py

@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+from .genconvit_ed import GenConViTED
+from .genconvit_vae import GenConViTVAE
+from torchvision import transforms
+
+class GenConViT(nn.Module):
+
+    def __init__(self, config, ed, vae, net, fp16):
+        super(GenConViT, self).__init__()
+        self.net = net
+        self.fp16 = fp16
+        if self.net=='ed':
+            try:
+                self.model_ed = GenConViTED(config)
+                self.checkpoint_ed = torch.load(f'weight/{ed}.pth', map_location=torch.device('cpu'))
+
+                if 'state_dict' in self.checkpoint_ed:
+                    self.model_ed.load_state_dict(self.checkpoint_ed['state_dict'])
+                else:
+                    self.model_ed.load_state_dict(self.checkpoint_ed)
+
+                self.model_ed.eval()
+                if self.fp16:
+                    self.model_ed.half()
+            except FileNotFoundError:
+                raise Exception(f"Error: weight/{ed}.pth file not found.")
+        elif self.net=='vae':
+            try:
+                self.model_vae = GenConViTVAE(config)
+                self.checkpoint_vae = torch.load(f'weight/{vae}.pth', map_location=torch.device('cpu'))
+
+                if 'state_dict' in self.checkpoint_vae:
+                    self.model_vae.load_state_dict(self.checkpoint_vae['state_dict'])
+                else:
+                    self.model_vae.load_state_dict(self.checkpoint_vae)
+                    
+                self.model_vae.eval()
+                if self.fp16:
+                    self.model_vae.half()
+            except FileNotFoundError:
+                raise Exception(f"Error: weight/{vae}.pth file not found.")
+        else:
+            try:
+                self.model_ed = GenConViTED(config)
+                self.model_vae = GenConViTVAE(config)
+                self.checkpoint_ed = torch.load(f'weight/{ed}.pth', map_location=torch.device('cpu'))
+                self.checkpoint_vae = torch.load(f'weight/{vae}.pth', map_location=torch.device('cpu'))
+                if 'state_dict' in self.checkpoint_ed:
+                    self.model_ed.load_state_dict(self.checkpoint_ed['state_dict'])
+                else:
+                    self.model_ed.load_state_dict(self.checkpoint_ed)
+                if 'state_dict' in self.checkpoint_vae:
+                    self.model_vae.load_state_dict(self.checkpoint_vae['state_dict'])
+                else:
+                    self.model_vae.load_state_dict(self.checkpoint_vae)
+                self.model_ed.eval()
+                self.model_vae.eval()
+                if self.fp16:
+                    self.model_ed.half()
+                    self.model_vae.half()
+            except FileNotFoundError as e:
+                raise Exception(f"Error: Model weights file not found.")
+
+
+    def forward(self, x):
+        if self.net == 'ed' :
+            x = self.model_ed(x)
+        elif self.net == 'vae':
+            x,_ = self.model_vae(x)
+        else:
+            x1 = self.model_ed(x)
+            x2,_ = self.model_vae(x)
+            x =  torch.cat((x1, x2), dim=0) #(x1+x2)/2 #
+        return x

model/genconvit_ed.py

@@ -0,0 +1,89 @@
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from timm import create_model
+import timm
+from .model_embedder import HybridEmbed
+
+class Encoder(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.features = nn.Sequential(    
+            nn.Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),
+
+            nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),
+
+            nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),
+
+            nn.Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)),
+
+            nn.Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0)
+        )
+
+    def forward(self, x):
+        return self.features(x)
+
+class Decoder(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+        self.features = nn.Sequential(
+            nn.ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2)),
+            nn.ReLU(inplace=True),
+
+            nn.ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2)),
+            nn.ReLU(inplace=True),
+
+            nn.ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2)),
+            nn.ReLU(inplace=True),
+
+            nn.ConvTranspose2d(32, 16, kernel_size=(2, 2), stride=(2, 2)),
+            nn.ReLU(inplace=True),
+
+            nn.ConvTranspose2d(16, 3, kernel_size=(2, 2), stride=(2, 2)),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        return self.features(x)
+
+class GenConViTED(nn.Module):
+    def __init__(self, config, pretrained=True):
+        super(GenConViTED, self).__init__()
+        self.encoder = Encoder()
+        self.decoder = Decoder()
+        self.backbone = timm.create_model(config['model']['backbone'], pretrained=pretrained)
+        self.embedder = timm.create_model(config['model']['embedder'], pretrained=pretrained)
+        self.backbone.patch_embed = HybridEmbed(self.embedder, img_size=config['img_size'], embed_dim=768)
+
+        self.num_features = self.backbone.head.fc.out_features * 2
+        self.fc = nn.Linear(self.num_features, self.num_features//4)
+        self.fc2 = nn.Linear(self.num_features//4, 2)
+        self.relu = nn.GELU()
+
+    def forward(self, images):
+
+        encimg = self.encoder(images)
+        decimg = self.decoder(encimg)
+
+        x1 = self.backbone(decimg)
+        x2 = self.backbone(images)
+
+        x = torch.cat((x1,x2), dim=1)
+
+        x = self.fc2(self.relu(self.fc(self.relu(x))))
+
+        return x

model/genconvit_vae.py

@@ -0,0 +1,116 @@
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from timm import create_model
+from model.config import load_config
+from .model_embedder import HybridEmbed
+
+config = load_config()
+
+class Encoder(nn.Module):
+
+    def __init__(self, latent_dims=4):
+        super(Encoder, self).__init__()
+
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(num_features=16),
+            nn.LeakyReLU(),
+            
+            nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(num_features=32),
+            nn.LeakyReLU(),
+            
+            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(num_features=64),
+            nn.LeakyReLU(),
+
+            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
+            nn.BatchNorm2d(num_features=128),
+            nn.LeakyReLU()
+        )
+
+        self.latent_dims = latent_dims
+        self.fc1 = nn.Linear(128*14*14, 256)
+        self.fc2 = nn.Linear(256, 128)
+        self.mu = nn.Linear(128*14*14, self.latent_dims)
+        self.var = nn.Linear(128*14*14, self.latent_dims)
+
+        self.kl = 0
+        self.kl_weight = 0.5#0.00025
+        self.relu = nn.LeakyReLU()
+
+    def reparameterize(self, x):
+        # https://github.com/AntixK/PyTorch-VAE/blob/a6896b944c918dd7030e7d795a8c13e5c6345ec7/models/vanilla_vae.py
+        std = torch.exp(0.5*self.mu(x))
+        eps = torch.randn_like(std)
+        z = eps * std + self.mu(x)
+
+        return z, std
+
+    def forward(self, x):
+        x = self.features(x)
+        x = torch.flatten(x, start_dim=1)
+
+        mu =  self.mu(x)
+        var = self.var(x)
+        z,_ = self.reparameterize(x)
+        self.kl = self.kl_weight*torch.mean(-0.5*torch.sum(1+var - mu**2 - var.exp(), dim=1), dim=0) 
+        
+        return z
+
+class Decoder(nn.Module):
+  
+    def __init__(self, latent_dims=4):
+        super(Decoder, self).__init__()
+
+        self.features = nn.Sequential(
+            nn.ConvTranspose2d(256, 64, kernel_size=2, stride=2),
+            nn.LeakyReLU(),
+
+            nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2),
+            nn.LeakyReLU(),
+
+            nn.ConvTranspose2d(32, 16, kernel_size=2, stride=2),
+            nn.LeakyReLU(),
+
+            nn.ConvTranspose2d(16, 3, kernel_size=2, stride=2),
+            nn.LeakyReLU()
+        )
+
+        self.latent_dims = latent_dims
+        
+        self.unflatten = nn.Unflatten(dim=1, unflattened_size=(256, 7, 7))
+
+    def forward(self, x): 
+        x = self.unflatten(x)
+        x = self.features(x)
+        return x
+        
+class GenConViTVAE(nn.Module):
+    def __init__(self, config, pretrained=True):
+        super(GenConViTVAE, self).__init__()
+        self.latent_dims = config['model']['latent_dims']
+        self.encoder = Encoder(self.latent_dims)
+        self.decoder = Decoder(self.latent_dims)
+        self.embedder = create_model(config['model']['embedder'], pretrained=True)
+        self.convnext_backbone = create_model(config['model']['backbone'], pretrained=True, num_classes=1000, drop_path_rate=0, head_init_scale=1.0)
+        self.convnext_backbone.patch_embed = HybridEmbed(self.embedder, img_size=config['img_size'], embed_dim=768)
+        self.num_feature = self.convnext_backbone.head.fc.out_features * 2
+ 
+        self.fc = nn.Linear(self.num_feature, self.num_feature//4)
+        self.fc3 = nn.Linear(self.num_feature//2, self.num_feature//4)
+        self.fc2 = nn.Linear(self.num_feature//4, config['num_classes'])
+        self.relu = nn.ReLU()
+        self.resize = transforms.Resize((224,224), antialias=True)
+
+    def forward(self, x):
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+
+        x1 = self.convnext_backbone(x)
+        x2 = self.convnext_backbone(x_hat)
+        x = torch.cat((x1,x2), dim=1)
+        x = self.fc2(self.relu(self.fc(self.relu(x))))
+        
+        return x, self.resize(x_hat)

model/model_embedder.py

@@ -0,0 +1,44 @@
+import torch
+import torch.nn as nn
+
+class HybridEmbed(nn.Module):
+    """ CNN Feature Map Embedding
+    Extract feature map from CNN, flatten, project to embedding dim.
+    """
+    def __init__(self, backbone, img_size=224, patch_size=1, feature_size=None, in_chans=3, embed_dim=768):
+        super().__init__()
+        assert isinstance(backbone, nn.Module)
+        img_size = (img_size, img_size)
+        patch_size = (patch_size, patch_size)
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.backbone = backbone
+        if feature_size is None:
+            with torch.no_grad():
+                # NOTE Most reliable way of determining output dims is to run forward pass
+                training = backbone.training
+                if training:
+                    backbone.eval()
+                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
+                if isinstance(o, (list, tuple)):
+                    o = o[-1]  # last feature if backbone outputs list/tuple of features
+                feature_size = o.shape[-2:]
+                feature_dim = o.shape[1]
+                backbone.train(training)
+        else:
+            feature_size = (feature_size, feature_size)
+            if hasattr(self.backbone, 'feature_info'):
+                feature_dim = self.backbone.feature_info.channels()[-1]
+            else:
+                feature_dim = self.backbone.num_features
+        assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
+        self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1])
+        self.num_patches = self.grid_size[0] * self.grid_size[1]
+        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x

model/pred_func.py

@@ -0,0 +1,115 @@
+import os
+import numpy as np
+import cv2
+import torch
+from torchvision import transforms
+from tqdm import tqdm
+from dataset.loader import normalize_data
+from .config import load_config
+from .genconvit import GenConViT
+from decord import VideoReader, cpu
+from .face_detection import detect_faces  # Import our new function
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def load_genconvit(config, net, ed_weight, vae_weight, fp16):
+    model = GenConViT(
+        config,
+        ed=ed_weight,
+        vae=vae_weight, 
+        net=net,
+        fp16=fp16
+    )
+
+    model.to(device)
+    model.eval()
+    if fp16:
+        model.half()
+
+    return model
+
+
+# Replace face_rec with our new function
+def face_rec(frames, p=None, klass=None):
+    return detect_faces(frames)
+
+
+def preprocess_frame(frame):
+    df_tensor = torch.tensor(frame, device=device).float()
+    df_tensor = df_tensor.permute((0, 3, 1, 2))
+
+    for i in range(len(df_tensor)):
+        df_tensor[i] = normalize_data()["vid"](df_tensor[i] / 255.0)
+
+    return df_tensor
+
+
+def pred_vid(df, model):
+    with torch.no_grad():
+        return max_prediction_value(torch.sigmoid(model(df).squeeze()))
+
+
+def max_prediction_value(y_pred):
+    # Finds the index and value of the maximum prediction value.
+    mean_val = torch.mean(y_pred, dim=0)
+    return (
+        torch.argmax(mean_val).item(),
+        mean_val[0].item()
+        if mean_val[0] > mean_val[1]
+        else abs(1 - mean_val[1]).item(),
+    )
+
+
+def real_or_fake(prediction):
+    return {0: "REAL", 1: "FAKE"}[prediction ^ 1]
+
+
+def extract_frames(video_file, frames_nums=15):
+    vr = VideoReader(video_file, ctx=cpu(0))
+    step_size = max(1, len(vr) // frames_nums)  # Calculate the step size between frames
+    return vr.get_batch(
+        list(range(0, len(vr), step_size))[:frames_nums]
+    ).asnumpy()  # seek frames with step_size
+
+
+def df_face(vid, num_frames, net):
+    img = extract_frames(vid, num_frames)
+    face, count = face_rec(img)
+    return preprocess_frame(face) if count > 0 else []
+
+
+def is_video(vid):
+    print('IS FILE', os.path.isfile(vid))
+    return os.path.isfile(vid) and vid.endswith(
+        tuple([".avi", ".mp4", ".mpg", ".mpeg", ".mov"])
+    )
+
+
+def set_result():
+    return {
+        "video": {
+            "name": [],
+            "pred": [],
+            "klass": [],
+            "pred_label": [],
+            "correct_label": [],
+        }
+    }
+
+
+def store_result(
+    result, filename, y, y_val, klass, correct_label=None, compression=None
+):
+    result["video"]["name"].append(filename)
+    result["video"]["pred"].append(y_val)
+    result["video"]["klass"].append(klass.lower())
+    result["video"]["pred_label"].append(real_or_fake(y))
+
+    if correct_label is not None:
+        result["video"]["correct_label"].append(correct_label)
+
+    if compression is not None:
+        result["video"]["compression"].append(compression)
+
+    return result

packages.txt

@@ -0,0 +1,6 @@
+build-essential
+cmake
+libopenblas-dev
+liblapack-dev
+libx11-dev
+libgtk-3-dev

prediction.py

@@ -0,0 +1,342 @@
+import os
+import argparse
+import json
+from time import perf_counter
+from datetime import datetime
+from model.pred_func import *
+from model.config import load_config
+
+config = load_config()
+print('CONFIG')
+print(config)
+def vids(
+    ed_weight, vae_weight, root_dir="sample_prediction_data", dataset=None, num_frames=15, net=None, fp16=False
+):
+    result = set_result()
+    r = 0
+    f = 0
+    count = 0
+    
+    model = load_genconvit(config, net, ed_weight, vae_weight, fp16)
+
+    for filename in os.listdir(root_dir):
+        curr_vid = os.path.join(root_dir, filename)
+
+        try:
+            if is_video(curr_vid):
+                result, accuracy, count, pred = predict(
+                    curr_vid,
+                    model,
+                    fp16,
+                    result,
+                    num_frames,
+                    net,
+                    "uncategorized",
+                    count,
+                )
+                f, r = (f + 1, r) if "FAKE" == real_or_fake(pred[0]) else (f, r + 1)
+                print(
+                    f"Prediction: {pred[1]} {real_or_fake(pred[0])} \t\tFake: {f} Real: {r}"
+                )
+            else:
+                print(f"Invalid video file: {curr_vid}. Please provide a valid video file.")
+
+        except Exception as e:
+            print(f"An error occurred: {str(e)}")
+
+    return result
+
+
+def faceforensics(
+    ed_weight, vae_weight, root_dir="FaceForensics\\data", dataset=None, num_frames=15, net=None, fp16=False
+):
+    vid_type = ["original_sequences", "manipulated_sequences"]
+    result = set_result()
+    result["video"]["compression"] = []
+    ffdirs = [
+        "DeepFakeDetection",
+        "Deepfakes",
+        "Face2Face",
+        "FaceSwap",
+        "NeuralTextures",
+    ]
+
+    # load files not used in the training set, the files are appended with compression type, _c23 or _c40
+    with open(os.path.join("json_file", "ff_file_list.json")) as j_file:
+        ff_file = list(json.load(j_file))
+
+    count = 0
+    accuracy = 0
+    model = load_genconvit(config, net, ed_weight, vae_weight, fp16)
+
+    for v_t in vid_type:
+        for dirpath, dirnames, filenames in os.walk(os.path.join(root_dir, v_t)):
+            klass = next(
+                filter(lambda x: x in dirpath.split(os.path.sep), ffdirs),
+                "original",
+            )
+            label = "REAL" if klass == "original" else "FAKE"
+            for filename in filenames:
+                try:
+                    if filename in ff_file:
+                        curr_vid = os.path.join(dirpath, filename)
+                        compression = "c23" if "c23" in curr_vid else "c40"
+                        if is_video(curr_vid):
+                            result, accuracy, count, _ = predict(
+                                curr_vid,
+                                model,
+                                fp16,
+                                result,
+                                num_frames,
+                                net,
+                                klass,
+                                count,
+                                accuracy,
+                                label,
+                                compression,
+                            )
+                        else:
+                            print(f"Invalid video file: {curr_vid}. Please provide a valid video file.")
+
+                except Exception as e:
+                    print(f"An error occurred: {str(e)}")
+
+    return result
+
+
+def timit(ed_weight, vae_weight, root_dir="DeepfakeTIMIT", dataset=None, num_frames=15, net=None, fp16=False):
+    keywords = ["higher_quality", "lower_quality"]
+    result = set_result()
+    model = load_genconvit(config, net, ed_weight, vae_weight, fp16)
+    count = 0
+    accuracy = 0
+    i = 0
+    for keyword in keywords:
+        keyword_folder_path = os.path.join(root_dir, keyword)
+        for subfolder_name in os.listdir(keyword_folder_path):
+            subfolder_path = os.path.join(keyword_folder_path, subfolder_name)
+            if os.path.isdir(subfolder_path):
+                # Loop through the AVI files in the subfolder
+                for filename in os.listdir(subfolder_path):
+                    if filename.endswith(".avi"):
+                        curr_vid = os.path.join(subfolder_path, filename)
+                        try:
+                            if is_video(curr_vid):
+                                result, accuracy, count, _ = predict(
+                                    curr_vid,
+                                    model,
+                                    fp16,
+                                    result,
+                                    num_frames,
+                                    net,
+                                    "DeepfakeTIMIT",
+                                    count,
+                                    accuracy,
+                                    "FAKE",
+                                )
+                            else:
+                                print(f"Invalid video file: {curr_vid}. Please provide a valid video file.")
+
+                        except Exception as e:
+                            print(f"An error occurred: {str(e)}")
+
+    return result
+
+
+def dfdc(
+    ed_weight,
+    vae_weight,
+    root_dir="deepfake-detection-challenge\\train_sample_videos",
+    dataset=None,
+    num_frames=15,
+    net=None,
+    fp16=False,
+):
+    result = set_result()
+    if os.path.isfile(os.path.join("json_file", "dfdc_files.json")):
+        with open(os.path.join("json_file", "dfdc_files.json")) as data_file:
+            dfdc_data = json.load(data_file)
+
+    if os.path.isfile(os.path.join(root_dir, "metadata.json")):
+        with open(os.path.join(root_dir, "metadata.json")) as data_file:
+            dfdc_meta = json.load(data_file)
+    model = load_genconvit(config, net, ed_weight, vae_weight, fp16)
+    count = 0
+    accuracy = 0
+    for dfdc in dfdc_data:
+        dfdc_file = os.path.join(root_dir, dfdc)
+
+        try:
+            if is_video(dfdc_file):
+                result, accuracy, count, _ = predict(
+                    dfdc_file,
+                    model,
+                    fp16,
+                    result,
+                    num_frames,
+                    net,
+                    "dfdc",
+                    count,
+                    accuracy,
+                    dfdc_meta[dfdc]["label"],
+                )
+            else:
+                print(f"Invalid video file: {dfdc_file}. Please provide a valid video file.")
+
+        except Exception as e:
+            print(f"An error occurred: {str(e)}")
+
+    return result
+
+
+def celeb(ed_weight, vae_weight, root_dir="Celeb-DF-v2", dataset=None, num_frames=15, net=None, fp16=False):
+    with open(os.path.join("json_file", "celeb_test.json"), "r") as f:
+        cfl = json.load(f)
+    result = set_result()
+    ky = ["Celeb-real", "Celeb-synthesis"]
+    count = 0
+    accuracy = 0
+    model = load_genconvit(config, net, ed_weight, vae_weight, fp16)
+
+    for ck in cfl:
+        ck_ = ck.split("/")
+        klass = ck_[0]
+        filename = ck_[1]
+        correct_label = "FAKE" if klass == "Celeb-synthesis" else "REAL"
+        vid = os.path.join(root_dir, ck)
+
+        try:
+            if is_video(vid):
+                result, accuracy, count, _ = predict(
+                    vid,
+                    model,
+                    fp16,
+                    result,
+                    num_frames,
+                    net,
+                    klass,
+                    count,
+                    accuracy,
+                    correct_label,
+                )
+            else:
+                print(f"Invalid video file: {vid}. Please provide a valid video file.")
+
+        except Exception as e:
+            print(f"An error occurred x: {str(e)}")
+
+    return result
+
+
+def predict(
+    vid,
+    model,
+    fp16,
+    result,
+    num_frames,
+    net,
+    klass,
+    count=0,
+    accuracy=-1,
+    correct_label="unknown",
+    compression=None,
+):
+    count += 1
+    print(f"\n\n{str(count)} Loading... {vid}")
+
+    df = df_face(vid, num_frames, net)  # extract face from the frames
+    if fp16:
+        df.half()
+    y, y_val = (
+        pred_vid(df, model)
+        if len(df) >= 1
+        else (torch.tensor(0).item(), torch.tensor(0.5).item())
+    )
+    result = store_result(
+        result, os.path.basename(vid), y, y_val, klass, correct_label, compression
+    )
+
+    if accuracy > -1:
+        if correct_label == real_or_fake(y):
+            accuracy += 1
+        print(
+            f"\nPrediction: {y_val} {real_or_fake(y)} \t\t {accuracy}/{count} {accuracy/count}"
+        )
+
+    return result, accuracy, count, [y, y_val]
+
+
+def gen_parser():
+    parser = argparse.ArgumentParser("GenConViT prediction")
+    parser.add_argument("--p", type=str, help="video or image path")
+    parser.add_argument(
+        "--f", type=int, help="number of frames to process for prediction"
+    )
+    parser.add_argument(
+        "--d", type=str, help="dataset type, dfdc, faceforensics, timit, celeb"
+    )
+    parser.add_argument(
+        "--s", help="model size type: tiny, large.",
+    )
+    parser.add_argument(
+        "--e", nargs='?', const='genconvit_ed_inference', default='genconvit_ed_inference', help="weight for ed.",
+    )
+    parser.add_argument(
+        "--v", '--value', nargs='?', const='genconvit_vae_inference', default='genconvit_vae_inference', help="weight for vae.",
+    )
+    
+    parser.add_argument("--fp16", type=str, help="half precision support")
+
+    args = parser.parse_args()
+    path = args.p
+    num_frames = args.f if args.f else 15
+    dataset = args.d if args.d else "other"
+    fp16 = True if args.fp16 else False
+
+    net = 'genconvit'
+    ed_weight = 'genconvit_ed_inference'
+    vae_weight = 'genconvit_vae_inference'
+
+    if args.e and args.v:
+        ed_weight = args.e
+        vae_weight = args.v
+    elif args.e:
+        net = 'ed'
+        ed_weight = args.e
+    elif args.v:
+        net = 'vae'
+        vae_weight = args.v
+    
+        
+    print(f'\nUsing {net}\n')  
+    
+
+    if args.s:
+        if args.s in ['tiny', 'large']:
+            config["model"]["backbone"] = f"convnext_{args.s}"
+            config["model"]["embedder"] = f"swin_{args.s}_patch4_window7_224"
+            config["model"]["type"] = args.s
+    
+    return path, dataset, num_frames, net, fp16, ed_weight, vae_weight
+
+
+def main():
+    start_time = perf_counter()
+    path, dataset, num_frames, net, fp16, ed_weight, vae_weight = gen_parser()
+    result = (
+        globals()[dataset](ed_weight, vae_weight, path, dataset, num_frames, net, fp16)
+        if dataset in ["dfdc", "faceforensics", "timit", "celeb"]
+        else vids(ed_weight, vae_weight, path, dataset, num_frames, net, fp16)
+    )
+
+    curr_time = datetime.now().strftime("%B_%d_%Y_%H_%M_%S")
+    file_path = os.path.join("result", f"prediction_{dataset}_{net}_{curr_time}.json")
+
+    with open(file_path, "w") as f:
+        json.dump(result, f)
+    end_time = perf_counter()
+    print("\n\n--- %s seconds ---" % (end_time - start_time))
+
+
+if __name__ == "__main__":
+    main()

result/data_april14_Celeb-DF.json

@@ -0,0 +1 @@
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result/data_april14_DFDC.json

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result/prediction_other_genconvit_March_07_2025_22_14_13.json

@@ -0,0 +1 @@
+{"video": {"name": ["0045.mp4.mp4"], "pred": [0.5602145195007324], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_07_2025_23_28_59.json

@@ -0,0 +1 @@
+{"video": {"name": ["0017_fake.mp4.mp4"], "pred": [0.9963172674179077], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_07_2025_23_33_10.json

@@ -0,0 +1 @@
+{"video": {"name": ["0017_fake.mp4.mp4"], "pred": [0.9963205456733704], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_07_2025_23_37_00.json

@@ -0,0 +1 @@
+{"video": {"name": ["0017_fake.mp4.mp4"], "pred": [0.9963181614875793], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_07_2025_23_55_27.json

@@ -0,0 +1 @@
+{"video": {"name": ["sample_1.mp4"], "pred": [0.9996999502182007], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_07_2025_23_59_40.json

@@ -0,0 +1 @@
+{"video": {"name": ["sample_1.mp4"], "pred": [0.9997004270553589], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_08_2025_00_03_44.json

@@ -0,0 +1 @@
+{"video": {"name": ["sample_1.mp4"], "pred": [0.9997005462646484], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_08_2025_00_10_00.json

@@ -0,0 +1 @@
+{"video": {"name": ["sample_1.mp4"], "pred": [0.999701976776123], "klass": ["uncategorized"], "pred_label": ["FAKE"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_08_2025_00_18_09.json

@@ -0,0 +1 @@
+{"video": {"name": ["anndvqgoko.mp4"], "pred": [0.05888557434082031], "klass": ["uncategorized"], "pred_label": ["REAL"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_08_2025_00_21_11.json

@@ -0,0 +1 @@
+{"video": {"name": ["anndvqgoko.mp4"], "pred": [0.05888569355010986], "klass": ["uncategorized"], "pred_label": ["REAL"], "correct_label": ["unknown"]}}

result/prediction_other_genconvit_March_08_2025_01_18_40.json

@@ -0,0 +1 @@
+{"video": {"name": ["anndvqgoko.mp4"], "pred": [0.058885395526885986], "klass": ["uncategorized"], "pred_label": ["REAL"], "correct_label": ["unknown"]}}

result_all.py

@@ -0,0 +1,75 @@
+import os
+import json
+import matplotlib.pyplot as plt
+from sklearn.metrics import roc_curve, roc_auc_score, f1_score
+
+json_files = [
+    os.path.join("result", "data_april14_Celeb-DF.json"),
+    os.path.join("result", "data_april14_DFDC.json"),
+    os.path.join("result", "data_april11_DeepfakeTIMIT.json"),
+    os.path.join("result", "data_april14_FF++.json"),
+]
+
+# Lists to store the ROC curve data
+fpr_list = []
+tpr_list = []
+roc_auc_list = []
+
+for json_file in json_files:
+    with open(json_file, "r") as f:
+        result = json.load(f)
+
+    # Get the actual labels and predicted probabilities or predicted labels from the result dictionary
+    actual_labels = result["video"]["correct_label"]
+    predicted_probs = result["video"]["pred"]
+    predicted_labels = result["video"]["pred_label"]
+
+    big_pp = [1 if P >= 0.5 else 0 for P in predicted_probs]
+    p_labels = [1 if label == "FAKE" else 0 for label in predicted_labels]
+    a_labels = [1 if label == "FAKE" else 0 for label in actual_labels]
+
+    # Calculate ROC curve and AUC
+    fpr, tpr, thresholds = roc_curve(a_labels, predicted_probs)
+    roc_auc = roc_auc_score(a_labels, predicted_probs)
+    f1 = f1_score(a_labels, big_pp)
+
+    # Append the data to the lists
+    fpr_list.append(fpr)
+    tpr_list.append(tpr)
+    roc_auc_list.append(roc_auc)
+
+    a = 0
+    for i in range(len(p_labels)):
+        if p_labels[i] == a_labels[i]:
+            a += 1
+
+    accuracy = sum(x == y for x, y in zip(p_labels, a_labels)) / len(p_labels)
+    real_acc = sum(
+        (x == y and y == 0) for x, y in zip(p_labels, a_labels)
+    ) / a_labels.count(0)
+    fake_acc = sum(
+        (x == y and y == 1) for x, y in zip(p_labels, a_labels)
+    ) / a_labels.count(1)
+    print(
+        f"{(json_file[:-5].split('_')[-1])}:\nReal accuracy {real_acc*100:.3f} Fake accuracy {fake_acc*100:.3f}, Accuracy: {accuracy*100:.3f}"
+    )
+    print(f"ROC AUC: {roc_auc:.3f}")
+    print(f"F1 Score: {f1:.3f}\n")
+
+# Plot ROC curves
+plt.figure()
+for i in range(len(json_files)):
+    plt.plot(
+        fpr_list[i],
+        tpr_list[i],
+        label=f"{json_files[i][:-5].split('_')[-1]} (area = %0.3f)" % roc_auc_list[i],
+    )
+
+plt.plot([0, 1], [0, 1], "k--")
+plt.xlim([0.0, 1.0])
+plt.ylim([0.0, 1.05])
+plt.xlabel("False Positive Rate")
+plt.ylabel("True Positive Rate")
+plt.title("Receiver Operating Characteristic (ROC) Curve")
+plt.legend(loc="lower right")
+plt.show()