Adversarial attacks aims to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks, imposing consistency checks on natural multi-object scenes is a promising yet practical defense against conventional adversarial attacks. More desired attacks, to this end, should be able to fool defenses with such consistency checks. Therefore, we present the first approach GLOW that copes with various attack requests by generating global layout-aware adversarial attacks where both categorical and geometric layout constraints are explicitly established. Specifically, we focus on object detection task and given a victim image, GLOW first localizes victim objects according to target labels. And then it generates multiple attack plans, together with their context-consistency scores. Our proposed GLOW, on the one hand, is capable of handling various types of requests, including single or multiple victim objects, with or without specified victim objects. On the other hand, it produces a consistency score for each attack plan, reflecting the overall contextual consistency that both semantic category and global scene layout are considered. In experiment, we design multiple types of attack requests and validate our ideas on MS COCO validation set. Extensive experimental results demonstrate that we can achieve about 40$\%$ average relative improvement compared to state-of-the-art methods in conventional single object attack request; Moreover, our method outperforms SOTAs significantly on more generic attack requests by at least 30$\%$; Finally, our method produces superior performance under challenging zero-query black-box setting, or 30$\%$ better than SOTAs. Our code, model and attack requests would be made available.

本文提出新的GLOW方法对多目标场景下单/多个受害者对象产生全局布局感知的分类和几何约束的对抗攻击，并与SOTAs和具有挑战性的无查询黑盒攻击的方法进行比较，结果表明在传统攻击请求的单个对象方面比SOTAs提高了约30％，在更广泛的攻击请求方面平均提高约20％。

全局布局感知物体检测攻击