This paper explores the connection between steganography and adversarial images. On the one hand, ste-ganalysis helps in detecting adversarial perturbations. On the other hand, steganography helps in forging adversarial perturbations that are not only invisible to the human eye but also statistically undetectable. This work explains how to use these information hiding tools for attacking or defending computer vision image classification. We play this cat and mouse game with state-of-art classifiers, steganalyzers, and steganographic embedding schemes. It turns out that steganography helps more the attacker than the defender.

翻译：本文探讨了线性图像和对立图像之间的联系。 一方面, 线性分析有助于发现对抗性扰动。 另一方面, 线性分析有助于形成对抗性扰动, 不仅对人的眼睛看不见, 而且在统计上也无法察觉。 这项工作解释了如何使用这些信息隐藏工具攻击或捍卫计算机视觉图像分类。 我们用最先进的分类器、 线性分析器和线性嵌入仪来玩猫和鼠游戏。 结果发现, 线性分析比防御者更能帮助攻击者。