Gradient Alignment for Cross-Domain Face Anti-Spoofing

The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024
[paper]

Overview

1. Installation

• Ubuntu 18.04.5 LTS
• CUDA 11.3
• Python 3.6.12
• pytorch == 1.10.1

2. Dataset

• Idiap Replay Attack [paper]
• OULU-NPU [paper]
• CASIA-MFSD [paper]
• MSU-MFSD [paper]

Data pre-processing: Follow the preprocessing steps in SAFAS.

3. Training

3.1 Running

Our implementaion of GAC-FAS is in optimizers/gacfas.py

Update configuration file and start training ICM2O by:

python train.py --config ./configs/ICM2O.yaml

Pre-trained weights are released.

3.2 Bag of tricks

Please consider following parameters while runing as it affects on final results (See our Suppl.)

• Random seed
• Leaning rate decay steps (40, 5)
• FC learning rate scale (1, 10)
• Logit scale (12, 16, 32)
• Weight decay (1e-4, 5e-4, 6e-4)
• ERM losses (BCE, CE: OMI2C)
• Color Jitter may help
• Balanced live vs. spoof data loader may help (only for OCI2M)
• Larger lr may help (1e-2: OMI2C)

3.3 Snapshot resutls

Methods	ICM2O		OCM2I		OCI2M		OMI2C
	HTER	AUC	HTER	AUC	HTER	AUC	HTER	AUC
MMD-AAE	40.98	63.08	31.58	75.18	27.08	83.19	44.59	58.29
MADDG	27.98	80.02	22.19	84.99	17.69	88.06	24.50	84.51
RFM	16.45	91.16	17.30	90.48	13.89	93.98	20.27	88.16
SSDG-M	25.17	81.83	18.21	94.61	16.67	90.47	23.11	85.45
SSDG-R	15.61	91.54	11.71	96.59	7.38	97.17	10.44	95.94
D2AM	15.27	90.87	15.43	91.22	12.70	95.66	20.98	85.58
SDA	23.10	84.30	15.60	90.10	15.40	91.80	24.50	84.40
DRDG	15.63	91.75	15.56	91.79	12.43	95.81	19.05	88.79
ANRL	15.67	91.90	16.03	91.04	10.83	96.75	17.85	89.26
SSAN	13.72	93.63	8.88	96.79	6.67	98.75	10.00	96.67
AMEL	11.31	93.96	18.60	88.79	10.23	96.62	11.88	94.39
EBDG	15.66	92.02	18.69	92.28	9.56	97.17	18.34	90.01
PathNet	11.82	95.07	13.40	95.67	7.10	98.46	11.33	94.58
IADG	8.86	97.14	10.62	94.50	5.41	98.19	8.70	96.40
SA-FAS	10.00	96.23	6.58	97.54	5.95	96.55	8.78	95.37
UDG-FAS	10.97	95.36	5.86	98.62	5.95	98.47	9.82	96.76
GAC-FAS (ours)	8.60 (0.28)	97.16 (0.40)	4.29 (0.83)	98.87 (0.60)	5.00 (0.00)	97.56 (0.06)	8.20 (0.43)	95.16 (0.09)

4. Landscape visualization

[paper][code][software]

Star (⭐) if you find it useful, and consider to cite our work

Citation

@inproceedings{le2024grad,
  title={Gradient Alignment for Cross-Domain Face Anti-Spoofing},
  author={Le, Binh M and Woo, Simon S},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}