PhySense: Defending Physically Realizable Attacks for Autonomous Systems via Consistency Reasoning
Zhiyuan Yu, Ao Li, Ruoyao Wen, Yijia Chen, Ning Zhang · 2024
Autonomous vehicles (AVs) empowered by deep neural networks (DNNs) are bringing transformative changes to our society. However, they are generally susceptible to adversarial attacks, especially physically realizable perturbations that can mislead perception and cause catastrophic outcomes. While existing defenses have shown success, there remains a pressing need for improved robustness while maintaining efficiency to meet real-time system operations.