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.

翻译：局部特征提取器是众多计算机视觉任务的基石。然而，其对对抗性攻击的脆弱性可能严重损害其有效性。本文探讨了攻击复杂局部特征提取算法与模型的方法，旨在实现两个不同目标：(1) 迫使原本不匹配的图像区域产生匹配；(2) 阻止原本匹配的区域实现匹配。在文末，我们讨论了不同补丁生成方法的性能与局限性。