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Provided is code that demonstrates the training and evaluation of the work presented in the paper: "Noise Modeling, Synthesis, and Classification for Generic Object Anti-Spoofing" published in CVPR 2020.

See the MSU CVLab website for project details and access to the GOSet dataset.

http://cvlab.msu.edu/project-goas.html

This code is provided as example code, and may not reflect a specific combination of hyper-parameters presented in the paper.

• prepare_dats.py: Processes the dataset into binary files for network training
• database.py: Reads prepared .dat files during network training
• networks.py: Defines the structure and operations of the networks
• golab_train.py: Trains the GOLab network
• golab_freeze.py: Optimizes and freezes the GOLab model for evaluation
• golab_eval.py: Evaluates the frozen GOLab model
• golab_perf.m: Compute evaluation metrics for GOLab
• gogen_train.py: Trains the GOGen network
• gogen_freeze.py: Optimizes and freezes the GOGen model for evaluation
• gogen_eval.py: Evaluates the frozen GOGen model
• gogen_perf.m: Compute evaluation metrics for GOGen

If you use or refer to this source code, please cite the following paper:

@inproceedings{cvpr2020-stehouwer,
  title={Noise Modeling, Synthesis, and Classification for Generic Object Anti-Spoofing},
  author={Joel Stehouwer, Amin Jourabloo, Yaojie Liu, Xiaoming Liu},
  booktitle={In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020)},
  address={Seattle, WA},
  year={2020}
}