rshaojimmy / OSAD

[ECCV 2020] Pytorch codes for Open-set Adversarial Defense

ECCV2020-OSAD

Pytorch codes for Open-set Adversarial Defense (pdf) in ECCV 2020

Challenges in open-set recogntion and adversarial robustness. (a) Conventional CNN classifiers fail in the presence of both open-set and adversarial images. (b) Open-set recognition methods can successfully identify open-set samples, but fail on adversarial samples. (c) Adversarial defense methods are unable to identify open-set samples. (d) Proposed method can identify open-set images and it is robust to adversarial images.

Thus this paper proposes a new research problem named Open-Set Adversarial Defense (OSAD) where adversarial attacks are studied under an open-set setting. We propose an Open-Set Defense Network (OSDN) that learns a latent feature space that is robust to adversarial attacks and informative to identify open-set samples.

Network structure of the proposed Open-Set Defense Network (OSDN). It consists of four components: encoder, decoder, open-set classifier and transformation classifier.

Setup

• Prerequisites: Python3.6, pytorch=1.2, Numpy, libmr

• The source code folders:

  1. "models": Contains the network architectures of proposed Open-Set Defense Network.
  2. "advertorch": Contains adversarial attacks such as FGSM and PGD. Thanks the codes from: https://github.com/BorealisAI/advertorch
  3. "OpensetMethods": Contains the open-max function. Thanks the codes from: https://github.com/lwneal/counterfactual-open-set
  4. "datasets": Contains datasets
  5. "misc": Contains initialization and some preprocessing functions

Training

To run the train file: python train.py

Testing

To run the test file: python test.py

It will generate .txt file that contains the score for close-set accuracy and AUROC.

Acknowledge

Please kindly cite this paper in your publications if it helps your research:

@inproceedings{shao2020open,
  title={Open-set adversarial defense},
  author={Shao, Rui and Perera, Pramuditha and Yuen, Pong C and Patel, Vishal M},
  booktitle={European Conference on Computer Vision},
  pages={682--698},
  year={2020},
  organization={Springer}
}

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