ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

Understand

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.