SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

Understand

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.