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Gradient Alignment for Cross-Domain Face Anti-Spoofing

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

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Overview

overall pipeline

1. Installation

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

2. Dataset

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]

overall pipeline

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}
}