Adversarial Patch for 3D Local Feature Extractor
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866929383100383232 |
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| author | Pao, Yu Wen Lai, Li Chang Lin, Hong-Yi |
| author_facet | Pao, Yu Wen Lai, Li Chang Lin, Hong-Yi |
| contents | Local feature extractors are the cornerstone of many computer vision tasks. However, their vulnerability to adversarial attacks can significantly compromise their effectiveness. This paper discusses approaches to attack sophisticated local feature extraction algorithms and models to achieve two distinct goals: (1) forcing a match between originally non-matching image regions, and (2) preventing a match between originally matching regions. At the end of the paper, we discuss the performance and drawbacks of different patch generation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08102 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adversarial Patch for 3D Local Feature Extractor Pao, Yu Wen Lai, Li Chang Lin, Hong-Yi Computer Vision and Pattern Recognition Local feature extractors are the cornerstone of many computer vision tasks. However, their vulnerability to adversarial attacks can significantly compromise their effectiveness. This paper discusses approaches to attack sophisticated local feature extraction algorithms and models to achieve two distinct goals: (1) forcing a match between originally non-matching image regions, and (2) preventing a match between originally matching regions. At the end of the paper, we discuss the performance and drawbacks of different patch generation methods. |
| title | Adversarial Patch for 3D Local Feature Extractor |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.08102 |