/Towards-Transferable-Targeted-Attack

Codes for CVPR2020 paper "Towards Transferable Targeted Attack".

Primary LanguagePython

Towards-Transferable-Targeted-Attack

Codes for CVPR2020 paper "Towards Transferable Targeted Attack".

checkpoints

The used normally trained and adversarially trained models are available in https://drive.google.com/drive/folders/16d58UIWfypX4QmXWhqKmD_nDcm-mDt68?usp=sharing

Please put them in the fold ./checkpoint.

Guide

incep_v3_trip_po.sh, incep_v3_ce.sh, and incep_v3_po.sh are three examples to run our attacks and the baselines. Everyone can change the python file names in these .sh files to attack different models.

Set the probability of DI2-FGSM, use --prob=0.7, --prob=0 means no diverse input pattern.

Set TI-FGSM, uncomment the line noise = tf.nn.depthwise_conv2d(noise, stack_kernel, strides=[1, 1, 1, 1], padding='SAME') in function graph(x, y, i, x_max, x_min, grad, y_target, y_logits).

Set MI-FGSM, use --prob=0 and comment out the line mentioned above.

Cite by

@inproceedings{li2020towards,
  title={Towards Transferable Targeted Attack},
  author={Li, Maosen and Deng, Cheng and Li, Tengjiao and Yan, Junchi and Gao, Xinbo and Huang, Heng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={641--649},
  year={2020}
}