DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches

DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches

The Thirty-ninth Annual Conference on Neural Information Processing Systems

https://openreview.net/forum?id=eD1VyYURiq

Yun Xing, Yue Cao, Nhat Chung, Jie Zhang, Ivor Tsang, Ming-Ming Cheng, Yang Liu, Lei Ma, Qing Guo

15 December 2025

Xing, Y., Cao, Y., Chung, N., Zhang, J., Tsang, I., Cheng, M., Liu, Y., Ma, L., & Guo, Q. (n.d.). DepthVanish: Optimizing adversarial interval structures for Stereo-Depth-Invisible patches. The Thirty-ninth Annual Conference on Neural Information Processing Systems

Stereo depth estimation is a critical task in autonomous driving and robotics, where inaccuracies (such as misidentifying nearby objects as distant) can lead to dangerous situations. Adversarial attacks against stereo depth estimation can help reveal vulnerabilities before deployment. Previous works have shown that repeating optimized textures can effectively mislead stereo depth estimation in digital settings. However, our research reveals that these naively repeated textures perform poorly in physical implementations, i.e., when deployed as patches, limiting their practical utility for stress-testing stereo depth estimation systems. In this work, for the first time, we discover that introducing regular intervals among the repeated textures, creating a grid structure, significantly enhances the patch’s attack performance. Through extensive experimentation, we analyze how variations of this novel structure influence the adversarial effectiveness. Based on these insights, we develop a novel stereo depth attack that jointly optimizes both the interval structure and texture elements. Our generated adversarial patches can be inserted into any scenes and successfully attack advanced stereo depth estimation methods of different paradigms, i.e., RAFT-Stereo and STTR. Most critically, our patch can also attack commercial RGB-D cameras (Intel RealSense) in real-world conditions, demonstrating their practical relevance for security assessment of stereo systems.

Publisher Copyright

This research / project is supported by the National Research Foundation, Singapore - AI Singapore Programme
Grant Reference no. : ISG4-GC-2023-008-1B

This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority - Trust Tech Funding Initiative
Grant Reference no. :

https://oar.a-star.edu.sg/communities-collections/articles/23012

1556-6013

Institute of High Performance Computing