Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917731960356864 |
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| author | Zhao, Chenxing Li, Yang Wu, Shihao Tan, Wenyi Zhou, Shuangju Pan, Quan |
| author_facet | Zhao, Chenxing Li, Yang Wu, Shihao Tan, Wenyi Zhou, Shuangju Pan, Quan |
| contents | Adversarial attacks against monocular depth estimation (MDE) systems pose significant challenges, particularly in safety-critical applications such as autonomous driving. Existing patch-based adversarial attacks for MDE are confined to the vicinity of the patch, making it difficult to affect the entire target. To address this limitation, we propose a physics-based adversarial attack on monocular depth estimation, employing a framework called Attack with Shape-Varying Patches (ASP), aiming to optimize patch content, shape, and position to maximize effectiveness. We introduce various mask shapes, including quadrilateral, rectangular, and circular masks, to enhance the flexibility and efficiency of the attack. Furthermore, we propose a new loss function to extend the influence of the patch beyond the overlapping regions. Experimental results demonstrate that our attack method generates an average depth error of 18 meters on the target car with a patch area of 1/9, affecting over 98\% of the target area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17312 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches Zhao, Chenxing Li, Yang Wu, Shihao Tan, Wenyi Zhou, Shuangju Pan, Quan Computer Vision and Pattern Recognition Adversarial attacks against monocular depth estimation (MDE) systems pose significant challenges, particularly in safety-critical applications such as autonomous driving. Existing patch-based adversarial attacks for MDE are confined to the vicinity of the patch, making it difficult to affect the entire target. To address this limitation, we propose a physics-based adversarial attack on monocular depth estimation, employing a framework called Attack with Shape-Varying Patches (ASP), aiming to optimize patch content, shape, and position to maximize effectiveness. We introduce various mask shapes, including quadrilateral, rectangular, and circular masks, to enhance the flexibility and efficiency of the attack. Furthermore, we propose a new loss function to extend the influence of the patch beyond the overlapping regions. Experimental results demonstrate that our attack method generates an average depth error of 18 meters on the target car with a patch area of 1/9, affecting over 98\% of the target area. |
| title | Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.17312 |