Improving Transferability of Adversarial Examples with Input Diversity
arXiv e-Print archive - 2018 via Local arXiv
cs.CV, cs.LG, stat.ML
First published: 2018/03/19 (3 years ago) Abstract: Though CNNs have achieved the state-of-the-art performance on various vision
tasks, they are vulnerable to adversarial examples --- crafted by adding
human-imperceptible perturbations to clean images. However, most of the
existing adversarial attacks only achieve relatively low success rates under
the challenging black-box setting, where the attackers have no knowledge of the
model structure and parameters. To this end, we propose to improve the
transferability of adversarial examples by creating diverse input patterns.
Instead of only using the original images to generate adversarial examples, our
method applies random transformations to the input images at each iteration.
Extensive experiments on ImageNet show that the proposed attack method can
generate adversarial examples that transfer much better to different networks
than existing baselines. By evaluating our method against top defense solutions
and official baselines from NIPS 2017 adversarial competition, the enhanced
attack 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%. We hope
that our proposed attack strategy can serve as a strong benchmark baseline for
evaluating the robustness of networks to adversaries and the effectiveness of
different defense methods in the future. Code is available at
Xie et al. propose to improve the transferability of adversarial examples by computing them based on transformed input images. In particular, they adapt I-FGSM such that, in each iteration, the update is computed on a transformed version of the current image with probability $p$. When, at the same time attacking an ensemble of networks, this is shown to improve transferability.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).