Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing

Understand

There is considerable evidence that deep neural networks are vulnerable to adversarial perturbations applied directly to their digital inputs.

• However, it remains an open question whether this translates to vulnerabilities in real systems.
• For example, an attack on self-driving cars would in practice entail modifying the driving environment, which then impacts the video inputs to the car's controller, thereby indirectly leading to incorrect driving decisions.
• Such attacks require accounting for system dynamics and tracking viewpoint changes.