# import streamlit as st # import numpy as np # import cv2 # import tempfile # import os # from PIL import Image # # ---- Page Configuration ---- # st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide") # st.title("📰 Fake News & Deepfake Detection Tool") # st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI") # # ---- Fake News Detection Section ---- # st.subheader("📝 Fake News Detection") # news_input = st.text_area("Enter News Text:", "Type here...") # if st.button("Check News"): # st.write("🔍 Processing...") # st.success("✅ Result: This news is FAKE.") # Replace with ML Model # # ---- Deepfake Image Detection Section ---- # st.subheader("📸 Deepfake Image Detection") # uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"]) # def compress_image(image, quality=90, max_size=(300, 300)): # ✅ High clarity image # img = Image.open(image).convert("RGB") # img.thumbnail(max_size) # temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") # img.save(temp_file.name, "JPEG", quality=quality) # return temp_file.name # if uploaded_image is not None: # compressed_image_path = compress_image(uploaded_image) # st.image(compressed_image_path, caption="🖼️ Compressed & Clear Image", use_column_width=True) # if st.button("Analyze Image"): # st.write("🔍 Processing...") # st.error("⚠️ Result: This image is a Deepfake.") # Replace with model # # ---- Deepfake Video Detection Section ---- # st.subheader("🎥 Deepfake Video Detection") # uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"]) # def compress_video(video): # temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") # with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video: # temp_video.write(video.read()) # video_path = temp_video.name # cap = cv2.VideoCapture(video_path) # if not cap.isOpened(): # st.error("❌ Error: Unable to read video!") # return None # fourcc = cv2.VideoWriter_fourcc(*'mp4v') # # ✅ New Resolution (100x80) & 15 FPS # frame_width = 50 # frame_height = 80 # out = cv2.VideoWriter(temp_file.name, fourcc, 15.0, (frame_width, frame_height)) # while cap.isOpened(): # ret, frame = cap.read() # if not ret: # break # frame = cv2.resize(frame, (frame_width, frame_height)) # out.write(frame) # cap.release() # out.release() # return temp_file.name # if uploaded_video is not None: # st.video(uploaded_video) # ✅ فوراً ویڈیو اپ لوڈ ہونے کے بعد دکھائیں # compressed_video_path = compress_video(uploaded_video) # if compressed_video_path: # st.video(compressed_video_path) # ✅ کمپریسڈ ویڈیو بھی دکھائیں # if st.button("Analyze Video"): # st.write("🔍 Processing...") # st.warning("⚠️ Result: This video contains Deepfake elements.") # Replace with model # st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**") import streamlit as st import numpy as np import cv2 import tempfile import os from PIL import Image import tensorflow as tf from transformers import pipeline from tensorflow.keras.applications import Xception, EfficientNetB7 from tensorflow.keras.models import Model from tensorflow.keras.layers import Dense, GlobalAveragePooling2D from tensorflow.keras.preprocessing.image import load_img, img_to_array # ---- Page Configuration ---- st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide") st.title("📰 Fake News & Deepfake Detection Tool") st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI") # Load Models fake_news_detector = pipeline("text-classification", model="microsoft/deberta-v3-base") # Load Deepfake Detection Models base_model_image = Xception(weights="imagenet", include_top=False) base_model_image.trainable = False # Freeze base layers x = GlobalAveragePooling2D()(base_model_image.output) x = Dense(1024, activation="relu")(x) x = Dense(1, activation="sigmoid")(x) # Sigmoid for probability output deepfake_image_model = Model(inputs=base_model_image.input, outputs=x) base_model_video = EfficientNetB7(weights="imagenet", include_top=False) base_model_video.trainable = False x = GlobalAveragePooling2D()(base_model_video.output) x = Dense(1024, activation="relu")(x) x = Dense(1, activation="sigmoid")(x) deepfake_video_model = Model(inputs=base_model_video.input, outputs=x) # Function to Preprocess Image def preprocess_image(image_path): img = load_img(image_path, target_size=(299, 299)) # Xception expects 299x299 img = img_to_array(img) img = np.expand_dims(img, axis=0) img /= 255.0 # Normalize pixel values return img # Function to Detect Deepfake Image def detect_deepfake_image(image_path): image = preprocess_image(image_path) prediction = deepfake_image_model.predict(image)[0][0] confidence = round(float(prediction), 2) label = "FAKE" if confidence > 0.5 else "REAL" return {"label": label, "score": confidence} # ---- Fake News Detection Section ---- st.subheader("📝 Fake News Detection") news_input = st.text_area("Enter News Text:", placeholder="Type here...") if st.button("Check News"): st.write("🔍 Processing...") prediction = fake_news_detector(news_input) label = prediction[0]['label'] confidence = prediction[0]['score'] if label == "FAKE": st.error(f"⚠️ Result: This news is FAKE. (Confidence: {confidence:.2f})") else: st.success(f"✅ Result: This news is REAL. (Confidence: {confidence:.2f})") # ---- Deepfake Image Detection Section ---- st.subheader("📸 Deepfake Image Detection") uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"]) if uploaded_image is not None: temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") img = Image.open(uploaded_image).convert("RGB") img.save(temp_file.name, "JPEG") st.image(temp_file.name, caption="🖼️ Uploaded Image", use_column_width=True) if st.button("Analyze Image"): st.write("🔍 Processing...") result = detect_deepfake_image(temp_file.name) if result["label"] == "FAKE": st.error(f"⚠️ Result: This image is a Deepfake. (Confidence: {result['score']:.2f})") else: st.success(f"✅ Result: This image is Real. (Confidence: {1 - result['score']:.2f})") # ---- Deepfake Video Detection Section ---- st.subheader("🎥 Deepfake Video Detection") uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"]) def detect_deepfake_video(video_path): cap = cv2.VideoCapture(video_path) frame_scores = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break frame_path = "temp_frame.jpg" cv2.imwrite(frame_path, frame) result = detect_deepfake_image(frame_path) frame_scores.append(result["score"]) os.remove(frame_path) cap.release() avg_score = np.mean(frame_scores) final_label = "FAKE" if avg_score > 0.5 else "REAL" return {"label": final_label, "score": round(float(avg_score), 2)} if uploaded_video is not None: st.video(uploaded_video) temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") with open(temp_file.name, "wb") as f: f.write(uploaded_video.read()) if st.button("Analyze Video"): st.write("🔍 Processing...") result = detect_deepfake_video(temp_file.name) if result["label"] == "FAKE": st.warning(f"⚠️ Result: This video contains Deepfake elements. (Confidence: {result['score']:.2f})") else: st.success(f"✅ Result: This video is Real. (Confidence: {1 - result['score']:.2f})") st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")