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Code Released: Adversarial Patch Training

The code for our paper on adversarial patch training on location-optimized adversarial patches is now available on GitHub. The repository includes a PyTorch implementation of our adversarial patch attack with location optimization as well as an adversarial training routine. The experiments on Cifar10 and GTSRB presented in the paper can easily be reproduced.

Adversarial patch training uses location-optimized adversarial patches during training to obtain robustness against adversarial patches at various locations within the image. For location-optimization various random and greedy heuristics are used. As result, adversarial patch training allows to obtain considerable robustness while not sacrificing accuracy.

The code for adversarial patch training is now available on GitHub:

Adversarial Patch Training on GitHub

The corresponding paper is available on ArXiv; also check out the project page maintained by Sukrut Rao:

Paper on ArXiv
@article{Rao2020ARXIV,
    author    = {Sukrut Rao and David Stutz and Bernt Schiele},
    title     = {Adversarial Training against Location-Optimized Adversarial Patches},
    journal   = {CoRR},
    volume    = {abs/1910.06259},
    year      = {2019}
}

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