Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attacks

2026-06-12

AI Summary

This work proposes SLASH, a novel physical adversarial attack that exploits microscopic scratches on camera lenses to induce structured optical artifacts under strong illumination or specular reflections, thereby causing significant errors in monocular depth estimation and 3D object detection. Unlike conventional image-space perturbations, SLASH models lens scratches as scene-triggered optical channels, representing a stealthy and persistent threat. By integrating optical modeling, physical experimentation, and deep learning, the method optimizes fixed scratch patterns to generate conditionally activated directional light streaks. Experiments demonstrate that, under realistic constraints of static scratches, the attack induces up to 32% relative error in depth estimation and substantially degrades 3D detection performance, with effective transferability across real-world camera systems and recorded video footage.

Abstract

Physical adversarial attacks on vision systems are typically studied through scene manipulation, such as adversarial patches or projections, where the adversary controls what the camera observes. Camera-side attacks using stickers or auxiliary optics have also been explored, but they treat attacks as image-space perturbations from designed patterns. This misses how physical imperfections interact with scene-dependent lighting and optics. We identify a threat: passive lens-side damage that is persistent yet trigger-conditioned, producing optical artifacts that bias geometric inference under particular visual conditions. We instantiate this threat through Scratch-induced Lens Adversarial Streak Hijacking SLASH, a physical-world attack caused by small scratches on a camera lens or protective cover. Scratches interact with bright light sources and specular reflections to create structured streak artifacts that distort depth cues. Since the perturbation is fixed in the optical path but triggered by the scene, it is both persistent and selective. We formulate the attack in optical space, model the scratch pattern as a trigger-conditioned optical channel, and optimize one fixed configuration across diverse viewing conditions. We evaluate SLASH on monocular depth estimation and monocular 3D object detection in digital and real-world settings. Under the fixed-scratch constraint, directional depth shifts reach up to 32% relative error for monocular depth estimation, with consistent effects on monocular 3D object detection. Physical experiments confirm transfer to real camera recordings, inducing depth shifts above the model's natural prediction baseline. These findings reveal an attack surface where benign-looking hardware imperfections act as latent, scene-triggered adversarial mechanisms, challenging assumptions about physical robustness and motivating defenses for secure vision systems.

Problem

• physical adversarial attacks
• camera-side optics
• scratch-induced artifacts
• monocular depth estimation
• optical perturbations

Innovation

• physical adversarial attack
• optical perturbation
• monocular depth estimation
• scratch-induced artifact
• scene-triggered attack