Vision Transformer for Face Anti-Spoofing on CelebA Spoof (PDA)
This model is a fine tuned Vision Transformer based face anti spoofing system designed to distinguish live faces from spoof attacks under real world conditions.
It is based on ViT Base Patch16 224 and evaluated on the CelebA Spoof PDA benchmark following the official protocol.
The model achieves strong discriminative performance with an AUC ROC of 0.9561 on the test splits.
This model is intended for research and benchmarking in face anti spoofing and face liveness detection.
Potential application domains include biometric authentication systems, access control, and academic evaluation of transformer based approaches for spoof detection.
The model is not intended for deployment in high risk security environments without additional validation, calibration, and fairness analysis.
Dataset
CelebA Spoof is a large scale face anti spoofing dataset containing diverse spoof attack types such as print, replay, and mask attacks.
To improve generalization, a GPU accelerated augmentation pipeline was implemented using Kornia.
Augmentations target variations in illumination, pose, blur, and camera artifacts.
Augmentation strategy:
Live samples: 8 augmented variants per image
Spoof samples: 2 augmented variants per image
Applied transformations include:
Random horizontal flip
Random rotation
Color jitter
Gaussian blur and noise
Perspective distortion
Elastic deformation
Sharpness adjustment
Normalization follows ImageNet statistics used by ViT models.
Model Architecture
The base Vision Transformer encoder is initialized with ImageNet pretrained weights.
A custom classification head is appended for binary classification.
The model was trained on augmented CelebA Spoof data using focal loss to address class imbalance.
Hyperparameters were optimized using Weights and Biases sweeps.
Training configuration:
Parameter
Value
Optimizer
AdamW
Learning rate
3e-4
Weight decay
0.05
Batch size
128
Epochs
50
Loss function
Focal Loss (alpha 0.25, gamma 2.0)
Scheduler
Cosine annealing with warmup
Early stopping
Enabled
Device
NVIDIA RTX A5000
Evaluation
Evaluation follows the CelebA Spoof PDA protocol using splits 19 to 21.
Threshold optimization was applied to balance false acceptance and false rejection rates.
Reported metrics include accuracy, F1 score, AUC ROC, precision, recall, specificity, FAR, FRR, and EER.
Results
Overall Performance
Metric
Value
Accuracy
0.8329
F1 Score
0.8780
AUC ROC
0.9561
Detection Metrics
Metric
Value
Precision
0.7974
Recall
0.9768
Specificity
0.6021
NPV
0.9417
Error Rates
Metric
Value
FAR
0.3979
FRR
0.0232
EER
0.1083
Confusion Matrix
Predicted Spoof
Predicted Live
Actual Spoof
404
267
Actual Live
25
1051
Limitations
Performance may degrade on datasets with significantly different capture conditions
High FAR indicates sensitivity to certain spoof patterns
No cross dataset evaluation included
Citation
If you use this model in your research, please cite the CelebA Spoof dataset and reference the repository:
