Web: http://arxiv.org/abs/2106.14300

Sept. 23, 2022, 1:24 a.m. | Ren Wang, Tianqi Chen, Philip Yao, Sijia Liu, Indika Rajapakse, Alfred Hero

cs.CR updates on arXiv.org arxiv.org

K-Nearest Neighbor (kNN)-based deep learning methods have been applied to
many applications due to their simplicity and geometric interpretability.
However, the robustness of kNN-based classification models has not been
thoroughly explored and kNN attack strategies are underdeveloped. In this
paper, we propose an Adversarial Soft kNN (ASK) loss to both design more
effective kNN attack strategies and to develop better defenses against them.
Our ASK loss approach has two advantages. First, ASK loss can better
approximate the kNN's probability of …

adversarial attack defense

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