Adversarial Patch for 3D Local Feature Extractor
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 12 Jun 2024.
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6 of 11 references.
References · 6
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,443
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,320
- 2023LightGlue: Local Feature Matching at Light Speedcited by 590
- 2017HPatches: A benchmark and evaluation of handcrafted and learned local descriptorscited by 264
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