Towards Evaluating Transfer-based Attacks Systematically, Practically,
  and Fairly
Qizhang Li Yiwen Guo Wangmeng Zuo Hao Chen
The adversarial vulnerability of deep neural networks (DNNs) has drawn great
attention due to the security risk of applying these models in real-world
applications. Based on transferability of adversarial examples, an increasing
number of transfer-based methods have been developed to fool black-box DNN
models whose architecture and parameters are inaccessible. Although tremendous
effort has been exerted, there still lacks a standardized benchmark that could
be taken advantage of to compare these methods systematically, fairly, and
practically. Our investigation shows that the evaluation of some methods needs
to be more reasonable and more thorough to verify their effectiveness, to
avoid, for example, unfair comparison and insufficient consideration of
possible substitute/victim models. Therefore, we establish a transfer-based
attack benchmark (TA-Bench) which implements 30+ methods. In this paper, we
evaluate and compare them comprehensively on 25 popular substitute/victim
models on ImageNet. New insights about the effectiveness of these methods are
gained and guidelines for future evaluations are provided. Code at:
https://github.com/qizhangli/TA-Bench.