Though deep neural models adopted to realize the perception of autonomous driving have proven vulnerable to adversarial examples, known attacks often leverage 2D patches and target mostly monocular perception. Therefore, the effectiveness of Physical Adversarial Examples (PAEs) on stereo-based binocular depth estimation remains largely unexplored. To this end, we propose the first texture-enabled physical adversarial attack against stereo matching models in the context of autonomous driving. Our method employs a 3D PAE with global camouflage texture rather than a local 2D patch-based one, ensuring both visual consistency and attack effectiveness across different viewpoints of stereo cameras. To cope with the disparity effect of these cameras, we also propose a new 3D stereo matching rendering module that allows the PAE to be aligned with real-world positions and headings in binocular vision. We further propose a novel merging attack that seamlessly blends the target into the environment through fine-grained PAE optimization. It has significantly enhanced stealth and lethality upon existing hiding attacks that fail to get seamlessly merged into the background. Extensive evaluations show that our PAEs can successfully fool the stereo models into producing erroneous depth information.

Modern Autonomous Vehicles (AVs) implement the Visual Perception Module (VPM) to perceive their surroundings. This VPM adopts various Deep Neural Network (DNN) models to process the data collected from cameras and LiDAR. Prior studies have shown that these models are vulnerable to physical adversarial examples (PAEs), which pose a critical safety risk to the autonomous driving task. While a few defense methods have been proposed to safeguard AVs, most of them only target a limited set of attack types and specific scenarios, making them impractical for real-world protection.