Cons-Def

Consensus Adversarial Defense Method Based on Augmented Examples

Anhui Normal University

School of Computer and Information, Wuhu, Anhui, China

241002

Adversarial defense, consensus defense, python, mnist, Cifar10, ImageNet This work is run for Consensus defense against FGSM, CW, PGD, and DeepFool attacks on MNIST, CIFAR-10, and ImageNet. Both white-attacks and black-attacks are implemented in this repository. Because CW attacks are implemented on encapsulated pb model, there is a code model_meta2pb.py to transform tensorflow model to pb model. There is also a stream file maker and fetch code cifar10_create_tf_record.py The repository is run under tensorflow-gpu-1.12.0, Python3.5.

This work is accepted in IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS for publication:

@article{ding2022consensus, title={Consensus Adversarial Defense Method Based on Augmented Examples}, author={Ding, Xintao and Cheng, Yongqiang and Luo, Yonglong and Li, Qingde and Gope, Prosanta}, journal={IEEE Transactions on Industrial Informatics}, year={2022}, publisher={IEEE} }

Thanks the authors of CleverHans, this demo is implemented based on some library functions from CleverHans.

Thanks the git user panjq, we borrowed Inecption model on ImageNet from his site.

xintaoding/Cons-Def

Cons-Def

Consensus Adversarial Defense Method Based on Augmented Examples