DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems

Understand

Deep Neural Networks (DNNs) have been widely applied in many autonomous systems such as autonomous driving.

• Recently, DNN testing has been intensively studied to automatically generate adversarial examples, which inject small-magnitude perturbations into inputs to test DNNs under extreme situations.
• While existing testing techniques prove to be effective, they mostly focus on generating digital adversarial perturbations (particularly for autonomous driving), e.g., changing image pixels, which may never happen in physical world.
• There is a critical missing piece in the literature on autonomous driving testing: understanding and exploiting both digital and physical adversarial perturbation generation for impacting steering decisions.