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EfficientNet — Preprocessing

Dataset

Storage location

  • Path: /home/gdgteam1/AI-Video-Detection/backend/dataset/AIGVDBench
  • Access command:
python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='AIGVDBench/AIGVDBench', repo_type='dataset', local_dir='./AIGVDBench')"

Data split

  • Train/Val/Test ratio: 70/15/15
  • Counts:
    • Train: 280000 videos
    • Val: 60000 videos
    • Test: 60000 videos
  • Method: Random split with seed 42; each unique real video and its derivatives remain in the same split.

Preprocessing steps

  1. Initialize MTCNN based on input arguments.
  2. Use OpenCV to split video into RGB frames.
  3. Pass frames into MTCNN to detect faces.

Code changes made for this preprocessing work

  • Updated backend/main.py
  • Updated backend/handlers/video_handler.py
  • Updated backend/preprocessing/video_processor.py
  • Added backend/models/DeepFake-EfficientNet/extract_face_efficientNet.sh
  • Added backend/models/DeepFake-EfficientNet/scripts/test.py
  • Modified backend/models/DeepFake-EfficientNet/scripts/train.py
  • Added backend/dataset/AIGVDBench/AIGVDBench/
  • Added backend/dataset/AIGVDBench/AIGVDBench/split_videos_left_out_easyAnimate.py
  • Added backend/dataset/AIGVDBench/AIGVDBench/split_videos_standard_split.py

How to run

# Step 1: Download/locate dataset
# Expected path: AI-Video-Detection/backend/dataset/AIGVDBench/AIGVDBench/

# Step 2: Split the dataset
cd backend/dataset/AIGVDBench/AIGVDBench
python3 split_videos_standard_split
# or
python3 split_videos_left_out_easyAnimate.py

# Step 3: Run preprocessing for EfficientNet
cd backend/models/DeepFake-EfficientNet
./extract_face_efficientNet.sh

Verification completed

  • Ran preprocessing end-to-end
  • Verified split counts match expectations
  • Verified processed data looks correct (sample check)
  • Confirmed no dataset files were committed
  • Updated documentation

Notes

  • To run preprocessing on a single video via main.py:
python3 -m backend.main --input-dir backend/dataset/AIGVDBench/AIGVDBench/Real/videos/_6E6r_nfgMU_40_113to214.mp4 --mode video
  • Expected output:
    • Initializing MTCNN...
    • FRAMES EXTRACTED!!!
    • FACES DETECTED!!!
    • BYE BYE!!!
  • The preprocessing in backend/main.py differs from the EfficientNet repo pipeline.
  • backend/main.py uses an MTCNN mode suitable for single-video processing and in-memory face handling.
  • The EfficientNet repo pipeline is designed for large-scale extraction and writes faces to directories.