One major factor impeding more widespread adoption of deep neural networks
(DNNs) is their lack of robustness, which is essential for safety-critical
applications such as autonomous driving. This has motivated much recent work on
adversarial attacks for DNNs, which mostly focus on pixel-level perturbations
void of semantic meaning. In contrast, we present a general framework for
adversarial attacks on trained agents, which covers semantic perturbations to
the environment of the agent performing the task as well as pixel-level
attacks. To do this, we re-frame the adversarial attack problem as learning a
distribution of parameters that always fools the agent. In the semantic case,
our proposed adversary (denoted as BBGAN) is trained to sample parameters that
describe the environment with which the black-box agent interacts, such that
the agent performs its dedicated task poorly in this environment. We apply
BBGAN on three different tasks, primarily targeting aspects of autonomous
navigation: object detection, self-driving, and autonomous UAV racing. On these
tasks, BBGAN can generate failure cases that consistently fool a trained agent.

为了解决深度神经网络的鲁棒性问题，文中提出了一种针对训练代理的通用框架，覆盖了对代理环境进行语义扰动和像素级攻击的情况，主要针对自主导航的几个方面，BBGAN 攻击可以生成一致愚弄训练代理的失败案例。