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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Sample.mp4 filter=lfs diff=lfs merge=lfs -text

Sample.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb48fbbfe295461889585a2c3ffe592ba208d2501018b9517f158108f11acd10
+size 11293922

app.py

@@ -0,0 +1,102 @@
+import streamlit as st
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import cv2
+import torch
+import numpy as np
+import tempfile
+
+image_processor = AutoImageProcessor.from_pretrained(
+    'ashish-001/deepfake-detection-using-ViT')
+model = AutoModelForImageClassification.from_pretrained(
+    'ashish-001/deepfake-detection-using-ViT')
+
+
+def classify_frame(frame):
+    inputs = image_processor(images=frame, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probs = torch.nn.functional.sigmoid(logits)
+    pred = torch.argmax(logits, dim=1).item()
+    lab = 'Real' if pred == 1 else 'Fake'
+    confidence, _ = torch.max(probs, dim=1)
+    return f"{lab}::{format(confidence.item(), '.2f')}"
+
+
+st.title("Deepfake detector")
+uploaded_file = st.file_uploader(
+    "Upload an image or video",
+    type=["jpg", "jpeg", "png", "mp4", "avi", "mov", "mkv"]
+)
+placeholder = st.empty()
+if st.button('Detect'):
+    if uploaded_file is not None:
+        clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+        mime_type = uploaded_file.type
+        if mime_type.startswith("image"):
+            file_bytes = uploaded_file.read()
+            np_arr = np.frombuffer(file_bytes, np.uint8)
+            image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            faces = clf.detectMultiScale(
+                gray, scaleFactor=1.3, minNeighbors=5)
+            for (x, y, w, h) in faces:
+                cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 0, 255), 2)
+                face = image_rgb[y:y + h, x:x + w]
+                img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+                label = classify_frame(img)
+                new_frame = cv2.putText(
+                    image_rgb, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+                st.image(new_frame)
+
+        elif mime_type.startswith('video'):
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
+                temp_file.write(uploaded_file.read())
+                temp_video_path = temp_file.name
+                cap = cv2.VideoCapture(temp_video_path)
+                if not cap.isOpened():
+                    st.error("Error: Cannot open video file.")
+                else:
+                    while True:
+                        ret, frame = cap.read()
+                        if not ret:
+                            break
+                        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                        faces = clf.detectMultiScale(
+                            gray, scaleFactor=1.3, minNeighbors=5)
+                        for (x, y, w, h) in faces:
+                            cv2.rectangle(
+                                frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
+                            face = frame[y:y + h, x:x + w]
+                            img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+                            label = classify_frame(img)
+                            frame = cv2.putText(
+                                frame, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+                        placeholder.image(frame)
+                    cap.release()
+
+if st.button('Use Example Video'):
+    clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+    cap = cv2.VideoCapture("Sample.mp4")
+    if not cap.isOpened():
+        st.error("Error: Cannot open video file.")
+    else:
+        st.write(f"Video credits: 'Deep Fakes' Are Becoming More Realistic Thanks To New Technology. Link:https://www.youtube.com/watch?v=CDMVaQOvtxU")
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = clf.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+            for (x, y, w, h) in faces:
+                cv2.rectangle(
+                    frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
+                face = frame[y:y + h, x:x + w]
+                img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+                label = classify_frame(img)
+                frame = cv2.putText(
+                    frame, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+            placeholder.image(frame)
+        cap.release()

requirements.txt

@@ -0,0 +1,5 @@
+streamlit==1.29.0
+transformers==4.35.2
+opencv-python==4.7.0.72
+torch==2.4.1
+numpy==1.23.5

test.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "e:\\miniconda\\lib\\site-packages\\transformers\\utils\\generic.py:441: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+      "  _torch_pytree._register_pytree_node(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoImageProcessor, AutoModelForImageClassification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "e:\\miniconda\\lib\\site-packages\\transformers\\utils\\generic.py:309: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\n",
+      "  _torch_pytree._register_pytree_node(\n"
+     ]
+    }
+   ],
+   "source": [
+    "image_processor = AutoImageProcessor.from_pretrained('model')\n",
+    "model = AutoModelForImageClassification.from_pretrained('model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a=[0.1,0.9]\n",
+    "print(np.max(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}