  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 and lane
changing, 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
movement, 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 self-driving 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.
