Dynamic Adversarial Attacks on Autonomous Driving Systems
Amirhosein Chahe, Chenan Wang, Abhishek S. Jeyapratap, Kaidi Xu, Lifeng Zhou · 2024
This paper introduces an attacking mechanism to challenge the resilience of autonomous driving systems.Specifically, we manipulate the decision-making processes of an autonomous vehicle by dynamically displaying adversarial patches on a screen mounted on another moving vehicle.These patches are optimized to deceive the object detection models into misclassifying targeted objects, e.g., traffic signs.Such manipulation has significant implications for critical multi-vehicle interactions such as intersection crossing, which are vital for safe and efficient autonomous driving systems.Particularly, we make four major contributions.First, we introduce a novel adversarial attack approach where the patch is not co-located with its target, enabling more versatile and stealthy attacks.Moreover, our method utilizes dynamic patches displayed on a screen, allowing for adaptive changes and movements, enhancing the flexibility and performance of the attack.To do so, we design a Screen Image Transformation Network (SIT-Net), which simulates environmental effects on the displayed images, narrowing the gap between simulated and real-world scenarios.Further, we integrate a positional loss term into the adversarial training process to increase the success rate of the dynamic attack.Finally, we shift the focus from merely attacking perceptual systems to influencing the decision-making algorithms of selfdriving systems.Our experiments demonstrate the first successful implementation of such dynamic adversarial attacks in real-world autonomous driving scenarios, paving the way for advancements in the field of robust and secure autonomous driving.