Counting 3,464 Big Data & Machine Learning Frameworks, Toolsets, and Examples...
Suggestion? Feedback? Tweet @stkim1

Author
Last Commit
Jan. 10, 2019
Created
Nov. 21, 2018

Trust Region Adversarial Attack

TRAttack is a pytorch library for Trust Region Based Adversarial Attack on neural networks. The library currently supports utility functions to compute adversarial examples for different neural network models.

Example Usage

First execute the following commands:

git clone git@github.com:amirgholami/TRAttack.git git submodule update --init

After training a neural network, the following command generates adversarial examples and computes the relative adversarial perturbation norm:

python trattack_cifar.py --resume cifar10_result/model_params.pkl --test-batch-size 1000 --worst-case 0 --iter 2000 --norm 8 --eps 0.001 --adap

For ImageNet, one can also use a pretrained model from pytorch as follows:

python trattack_imagenet.py -a resnet50 --pretrained --batch-size 1 --worst-case 0 --iter 5000 --norm 8 --eps 0.0001 --class 9 --plotting image_example/ --adap

Citation

TRAttack has been developed as part of the following paper. If you found the library useful for your work, we appreciate if you would please cite the following paper:

  • Z Yao, A Gholami, P Xu, K Keutzer, MW Mahoney. Trust Region Based Adversarial Attack on Neural Networks, PDF