video detector

app.py

@@ -1,22 +1,95 @@
 import gradio as gr
+from detector.face import verify_faces, analyze_face
+from detector.voice import verify_voices
+from detector.video import verify_faces_in_video
+
 
 def start_scan(image, audio):
     return "Scanning in progress...", None
 
-with gr.Blocks(title="Deepfake Watchdog") as app:
-    gr.Markdown("## 🛡️ Deepfake Watchdog\nUpload your image and/or voice to scan for deepfake misuse online.")
-    
-    with gr.Row():
+
+def compare_faces(img1_path, img2_path):
+    result = verify_faces(img1_path, img2_path)
+
+    if "error" in result:
+        return f"❌ Error: {result['error']}"
+
+    if result["verified"]:
+        return f"✅ Match! Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
+    else:
+        return f"❌ No Match. Distance: {result['distance']:.4f} (Threshold: {result['threshold']})"
+
+
+def compare_voices(audio1, audio2):
+    result = verify_voices(audio1, audio2)
+
+    if "error" in result:
+        return f"❌ Error: {result['error']}"
+
+    if result["match"]:
+        return f"✅ Same speaker detected. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
+    else:
+        return f"❌ Different speakers. Similarity: {result['similarity']} (Threshold: {result['threshold']})"
+
+
+def scan_video(video_path, ref_img):
+    results = verify_faces_in_video(video_path, ref_img)
+    report = ""
+
+    for r in results:
+        if "error" in r:
+            report += f"\n⚠️ Frame {r['frame']}: {r['error']}"
+        else:
+            status = "✅ Match" if r["verified"] else "❌ Mismatch"
+            report += f"\n🖼 Frame {r['frame']}: {status} (Distance: {r['distance']})"
+
+    return report
+
+
+
+with gr.Blocks(title="Deepfake Watchdog") as demo:
+    gr.Markdown("# 🛡️Deepfake Watchdog 🤗")
+    gr.Markdown("## Upload your image and/or voice to scan for deepfake misuse online.")
+
+    # Face Verification
+    gr.Markdown("### 📷 Face Verification")
+    with gr.Tab("Face Verification"):
         image = gr.Image(label="Upload your face", type="filepath")
         audio = gr.Audio(label="Upload your voice (optional)", type="filepath")
-        
+
     run_button = gr.Button("Start Scan")
     output_text = gr.Textbox(label="Status")
     output_gallery = gr.Gallery(label="Matched Results")
-    
-    run_button.click(start_scan, inputs=[image, audio], outputs=[output_text, output_gallery])
-    
+
+    run_button.click(
+        start_scan, inputs=[image, audio], outputs=[output_text, output_gallery]
+    )
+
+    # Voice Verification
+    gr.Markdown("### 🎤 Voice Verification")
+    with gr.Tab("🎤 Voice Verification"):
+        gr.Markdown("Upload two audio files to check if the voices match.")
+
+        audio1 = gr.Audio(type="filepath", label="Voice Sample 1")
+        audio2 = gr.Audio(type="filepath", label="Voice Sample 2")
+
+        voice_btn = gr.Button("Compare Voices")
+        voice_output = gr.Textbox(label="Result")
+
+        voice_btn.click(compare_voices, inputs=[audio1, audio2], outputs=voice_output)
+        
+    # Video DeepFake Scan
+    gr.Markdown("### 📹 Video Deepfake Scan")
+    with gr.Tab("📹 Video Deepfake Scan"):
+        gr.Markdown("Upload a video and a reference image. We'll scan for deepfake face mismatches.")
+
+        ref_img = gr.Image(type="filepath", label="Reference Face")
+        video_input = gr.Video(label="Video File")
+        scan_btn = gr.Button("Scan Video")
+        scan_output = gr.Textbox(label="Scan Results", lines=10)
+
+        scan_btn.click(scan_video, inputs=[video_input, ref_img], outputs=scan_output)
 
 
 
-app.launch(mcp_server=True)
+demo.launch(mcp_server=True)

detector/video.py

@@ -0,0 +1,57 @@
+from deepface import DeepFace
+import cv2
+import os
+
+
+def extract_frames(video_path, interval=30):
+    """
+    Extracts frames from a video at a specified interval.
+    """
+    # Open the video file
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    count = 0
+
+    # Loop through the video frames
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        #
+        if count % interval == 0:
+            frame_path = f"/tmp/frame_{count}.jpg"
+            cv2.imwrite(frame_path, frame)
+            frames.append(frame_path)
+        count += 1
+
+    # Release the video capture object
+    cap.release()
+
+    return frames
+
+
+def verify_faces_in_video(video_path, reference_img, interval=30, threshold=0.7):
+    """
+    Verifies if faces in a video match a reference image.
+    """
+    results = []
+    # Extract frames from the video
+    frames = extract_frames(video_path, interval)
+
+    # Loop through the frames
+    for frame_path in frames:
+        # Perform face verification
+        try:
+            # Perform face verification
+            result = DeepFace.verify(
+                img1_path=reference_img, img2_path=frame_path, enforce_detection=False
+            )
+            score = result["distance"]
+            verified = result["verified"]
+            results.append(
+                {"frame": frame_path, "distance": round(score, 4), "verified": verified}
+            )
+        except Exception as e:
+            results.append({"frame": frame_path, "error": str(e)})
+    return results