Computer Vision · 2025
Multi-Attentional Deepfake Detection
Attention-guided detection over a ConvNeXt backbone
A deepfake classifier that combines local attention, global context, and texture evidence while exposing where the network looks.
- Python
- PyTorch
- timm
- Streamlit
- ConvNeXt
Measured evidence
- evaluation accuracy
- 85.66%
- FAKE-class F1 score
- 0.9228
- weighted F1 score
- ≈0.7905
Problem worth solving
Classify face images as real or fake while exposing visual evidence that helps inspect what the network attends to.
Engineering decisions
- Used a ConvNeXt backbone with local attention pooling, global feature extraction, and texture enhancement.
- Combined the extracted signals in an ensemble classifier.
- Built a Streamlit demo with image upload, remote checkpoint loading, prediction output, and attention-map visualization.
What changed
- Reached 85.66% accuracy on the reported evaluation set.
- Recorded a 0.9228 FAKE-class F1 score and an approximately 0.7905 weighted F1 score.