  As physical adversarial attacks become extensively applied in unearthing the
potential risk of security-critical scenarios, especially in autonomous
driving, their vulnerability to environmental changes has also been brought to
light. The non-robust nature of physical adversarial attack methods brings
less-than-stable performance consequently. To enhance the robustness of
physical adversarial attacks in the real world, instead of statically
optimizing a robust adversarial example via an off-line training manner like
the existing methods, this paper proposes a brand new robust adversarial attack
framework: Embodied Adversarial Attack (EAA) from the perspective of dynamic
adaptation, which aims to employ the paradigm of embodied intelligence:
Perception-Decision-Control to dynamically adjust the optimal attack strategy
according to the current situations in real time. For the perception module,
given the challenge of needing simulation for the victim's viewpoint, EAA
innovatively devises a Perspective Transformation Network to estimate the
target's transformation from the attacker's perspective. For the decision and
control module, EAA adopts the laser-a highly manipulable medium to implement
physical attacks, and further trains an attack agent with reinforcement
learning to make it capable of instantaneously determining the best attack
strategy based on the perceived information. Finally, we apply our framework to
the autonomous driving scenario. A variety of experiments verify the high
effectiveness of our method under complex scenes.

Computer Vision and Pattern Recognition