qilong-zhang / Targeted_Patch-wise-plusplus_iterative_attack

The extension of "Patch-wise Attack for Fooling Deep Neural Network (ECCV2020)", and we aim to boost the success rates of targeted attack.

Patch-wise++ Iterative Targeted Attack

This is the Tensorflow code for our paper Patch-wise++ Perturbation for Adversarial Targeted Attacks, and Pytorch version can be easily extended from here.

This paper is the extension of Patch-wise Attack for Fooling Deep Neural Network, and we aim to boost the success rates of targeted attack. Consider targeted attacks aim to push the adversarial examples into the territory of a specific class, and the amplification factor may lead to underfitting. Thus, we introduce the temperature and propose a patch-wise++ iterative method (PIM++) to further improve transferability without significantly sacrificing the performance of the white-box attack. Compared with the current state-of-the-art attack methods, our DTPI-FGSM++ significantly improves the success rate by 33.1% for defense models and 31.4% for normally trained models on average.

Implementation

• Tensorflow 1.14, gast 0.2.2, Python3.7

• Download the models

  • Normlly trained models
  • Ensemble adversarial trained models

• Then put these models into ".models/"

• We give two attack codes for you to implement our attacks. For example, if the hold-out model is normally trained model, we can run the follow code:

  python DTPI-FGSM++_for_NT.py

  and another code is for the above Ensemble adversarial trained models.

• The output images are in "output/"

Citing this work

If you find this work is useful in your research, please consider citing:

@inproceedings{GaoZhang2020PatchWise++,
    title={Patch-wise++ Perturbation for Adversarial Targeted Attacks},
    author={Gao, Lianli and Zhang, Qilong and Song, Jingkuan and Shen, Hengtao},
    journal   = {CoRR},
    volume    = {abs/2012.15503},
    year      = {2020},
}