PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving

Understand

Although Deep neural networks (DNNs) are being pervasively used in vision-based autonomous driving systems, they are found vulnerable to adversarial attacks where small-magnitude perturbations into the inputs during test time cause dramatic changes to the outputs.

• While most of the recent attack methods target at digital-world adversarial scenarios, it is unclear how they perform in the physical world, and more importantly, the generated perturbations under such methods would cover a whole driving scene including those fixed background imagery such as the sky, making them inapplicable to physical world implementation.
• We present PhysGAN, which generates physical-world-resilient adversarial examples for mislead-ing autonomous driving systems in a continuous manner.
• We show the effectiveness and robustness of PhysGAN via extensive digital and real-world evaluations.