论文标题
基于线性回归的中级攻击框架
An Intermediate-level Attack Framework on The Basis of Linear Regression
论文作者
论文摘要
本文实质上扩展了我们在ECCV上发表的工作，其中提出了中级攻击以提高某些基线对抗示例的可传递性。具体而言，我们提倡一个框架，在该框架中，从中间级别差异（对抗特征和良性特征之间）的直接线性映射到建立了对抗性示例的预测丢失。通过深入研究这种框架的核心组成部分，我们表明1）可以考虑各种线性回归模型，以建立映射，2）最终获得的最终获得的中间级别的对手差异的幅度与转移性相关，3）可以通过随机的多次运行来实现多个基线攻击的进一步增强性能。此外，通过利用这些发现，我们在基于转移的$ \ ell_ \ infty $和$ \ ell_2 $攻击方面实现了新的最先进。我们的代码可在https://github.com/qizhangli/ila-plus-plus-lr上公开获取。
This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples. Specifically, we advocate a framework in which a direct linear mapping from the intermediate-level discrepancies (between adversarial features and benign features) to prediction loss of the adversarial example is established. By delving deep into the core components of such a framework, we show that 1) a variety of linear regression models can all be considered in order to establish the mapping, 2) the magnitude of the finally obtained intermediate-level adversarial discrepancy is correlated with the transferability, 3) further boost of the performance can be achieved by performing multiple runs of the baseline attack with random initialization. In addition, by leveraging these findings, we achieve new state-of-the-arts on transfer-based $\ell_\infty$ and $\ell_2$ attacks. Our code is publicly available at https://github.com/qizhangli/ila-plus-plus-lr.
