Stereo cameras are integrated into autonomous systems such as self-driving cars, drones, and robots to offer precise depth estimation in a cost-effective manner compared to LiDAR technology. In this work, we reveal an intrinsic vulnerability in stereo cameras that stems from their pixel sampling and calibration processes, which can influence the outputs of stereo matching algorithms. Attackers can achieve fine-grained control over the estimated depth of real obstacles using simple repeating patterns, without relying on sophisticated adversarial machine learning techniques. Furthermore, deep learning-based depth estimation models exhibit a similar vulnerability. We evaluate the impact of this attack on two widely used stereo matching algorithms (BM and SGBM), three deep learning models (PSMNet, MoCha-Stereo, and UniMatch), a stereo-LiDAR fusion model (SGM-DDC), and two popular commercial stereo cameras, the ZED2 and Intel RealSense D435. For example, in the ZED2 camera, an attacker can displace obstacles up to 20~meters farther or 12~meters closer. In our real-world evaluation in a driving setting, a brief 0.5~second attack can trigger emergency braking in a popular autonomous driving framework. We further demonstrate the feasibility at driving speeds up to 40~km/h using CARLA. Finally, we confirm the ineffectiveness of state-of-the-art defenses, and we propose a novel strategy that leverages similarity scores to dynamically detect and suppress the depth discrepancies. Our work highlights vulnerabilities hidden in stereo matching and deep learning depth estimation models, addressing critical limitations in autonomous system deployments.

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