  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.
