GUAP: Generalizing Universal Adversarial Attacks Beyond Additive Perturbations

Tool for generating spatial-transfermed or additive universarial perturbations, the paper 'Generalizing Universal Adversarial Attacks Beyond Additive Perturbations' was accepted by ICDM 2020.

Please cite Yanghao Zhang, Wenjie Ruan, Fu Wang, and Xiaowei Huang, Generalizing Universal Adversarial Attacks Beyond Additive Perturbations, The IEEE International Conference on Data Mining (ICDM 2020), November 17-20, 2020, Sorrento, Italy

In this paper, for the first time we propose a unified and flexible framework, which can capture the distribution of the unknown additive and non-additive adversarial perturbations jointly for crafting Generalized Universal Adversarial Perturbations. Specifically, GUAP can generate either additive (i.e., l_inf-bounded) or non-additive (i.e., spatial transformation) perturbations, or a com- bination of both, which considerably generalizes the attacking capability of current universal attack methods.

Running environment:

python 3.6.10

pytorch 1.5.0

Colab demo:

There is also a notebook demo Colab_GUAP.ipynb, which can be run on the Colab.

Generalizing UAP for Cifar10:

	python run_cifar --gpuid 0 --model VGG19

Generalizing UAP for ImageNet:

	python run_imagenet.py --gpuid 0,1 --model ResNet152

Experimental results:

Note: This work is accepted by ICDM 2020. Pls find the paper here: Generalizing Universal Adversarial Attacks Beyond Additive Perturbations

-- Yanghao Zhang & Wenjie Ruan