EpiMirror: Epipolar Correspondence Mirrored Perturbations Against Stereo Matching

To understand the robustness of stereo-based 3D perception, adversarial perturbations against stereo matching have been increasingly studied. However, existing attacks do not exploit any specific vulnerability in this task, but simply follow conventional attacks (originally developed against image classification) that target final prediction errors or internal feature discrepancies. In this paper, we address this limitation by instead targeting epipolar structure, the task-specific geometric constraint on stereo correspondence. Specifically, we propose **EpiMirror**, a new attack that constructs **Mirror**ed perturbations along the **Epi**polar line, i.e., perturbations with pixel-wise opposite directions (but identical amplitude) for left vs. right views. In this way, EpiMirror explicitly disrupts cross-view matching, subsequently promoting disparities along the same epipolar line, and ultimately leading to erroneous depth estimation. Experimental results across six stereo architectures and four datasets show that EpiMirror improves the D1-error of existing white-box, black-box transfer-based, and black-box query-based attacks by (relative) 16.9%, 20.2%, and 79.4% on average. We further provide a theoretical analysis attributing the success of mirrored perturbations to amplification of correspondence mismatch and collapse of matching margin.