This paper proposed to improve the transferability of adversarial examples by creating diverse input patterns (https://arxiv.org/abs/1803.06978). Instead of only using the original images to generate adversarial examples, the proposed method, Diverse Input Iterative Fast Gradient Sign Method (DI2-FGSM), applies random transformations to the input images at each iteration. The generated adversarial examples are much more transferable than those generated by FGSM and I-FGSM. An example is shown below:
To improve the transferability further, we
- integrate momentum term into the attack process (https://arxiv.org/abs/1710.06081);
- attack multiple networks simultaneously (https://arxiv.org/abs/1611.02770).
By evaluating this enhanced attack w.r.t. the top 3 defense submissions and 3 official baselines from NIPS 2017 adversarial competition (https://www.kaggle.com/c/nips-2017-non-targeted-adversarial-attack), it reaches an average success rate of 73.0%, which outperforms the top 1 attack submission in the NIPS competition by a large margin of 6.6%. Please refer to the Table 3 in the paper for details.
Different attacks can be related via different parameter settings, as shown below:
- For the implementations of random resizing and random padding (https://arxiv.org/abs/1711.01991), the original version is available at https://github.com/cihangxie/NIPS2017_adv_challenge_defense. We adopt a more user-friendly re-implementation https://github.com/anishathalye/obfuscated-gradients in our repo only for releasing purpose.
If you find this work is useful in your research, please consider citing:
@inproceedings{xie2019improving,
title={Improving Transferability of Adversarial Examples with Input Diversity},
author={Xie, Cihang and Zhang, Zhishuai and Zhou, Yuyin and Bai, Song and Wang, Jianyu and Ren, Zhou and Yuille, Alan},
Booktitle = {Computer Vision and Pattern Recognition},
year={2019},
organization={IEEE}
}
