Staircase Sign Method for Boosting Adversarial Attacks
Qilong Zhang, Xiaosu Zhu, Jingkuan Song, Lianli Gao, and Heng Tao Shen

TL;DR
This paper introduces the Staircase Sign Method (S$^2$M), a novel gradient manipulation technique that enhances the transferability and effectiveness of adversarial attacks without significant computational cost.
Contribution
The paper proposes S$^2$M, which segments gradient signs into multiple levels with staircase weights, improving adversarial attack transferability over traditional Sign Method.
Findings
Significantly improves transferability of adversarial examples.
Effective in both white-box and black-box attack scenarios.
Achieves an average transferability increase of 5.1% and 12.8% on ImageNet.
Abstract
Crafting adversarial examples for the transfer-based attack is challenging and remains a research hot spot. Currently, such attack methods are based on the hypothesis that the substitute model and the victim model learn similar decision boundaries, and they conventionally apply Sign Method (SM) to manipulate the gradient as the resultant perturbation. Although SM is efficient, it only extracts the sign of gradient units but ignores their value difference, which inevitably leads to a deviation. Therefore, we propose a novel Staircase Sign Method (SM) to alleviate this issue, thus boosting attacks. Technically, our method heuristically divides the gradient sign into several segments according to the values of the gradient units, and then assigns each segment with a staircase weight for better crafting adversarial perturbation. As a result, our adversarial examples perform better in…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Domain Adaptation and Few-Shot Learning
