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Improving Transferability of Adversarial Examples with Input Diversity
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README.md

Improving Transferability of Adversarial Examples with Input Diversity

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:

demo

Extension

To improve the transferability further, we

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.

Relationships between different attacks

Different attacks can be related via different parameter settings, as shown below:

Inception_v3 model

Acknowledgements

Citing this work

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}
}
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