Upload main.py

main.py

@@ -61,11 +61,11 @@ def extract_faces_from_video(video_path, max_frames=8):
         if boxes is not None and len(boxes) > 0:
             box = boxes[0]
             
-            # Add 30% padding around the face. Deepfake models need to see the jawline and background boundaries!
+            # Add 10% padding around the face. Reduced from 30% to prevent background noise interference.
             w = box[2] - box[0]
             h = box[3] - box[1]
-            pad_w = int(w * 0.3)
-            pad_h = int(h * 0.3)
+            pad_w = int(w * 0.1)
+            pad_h = int(h * 0.1)
             
             # Ensure box coordinates are within image bounds with padding
             x1 = max(0, int(box[0]) - pad_w)
@@ -128,7 +128,7 @@ async def analyze_video(file: UploadFile = File(...)):
         # Get average probability for 'Fake' class across the worst frames
         avg_fake_prob = top_k_probs.mean().item()
         
-        is_fake = avg_fake_prob > 0.5
+        is_fake = avg_fake_prob > 0.75
         
         # Calculate confidence score based on the chosen class
         if is_fake: