AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients

Understand

Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible perturbations.

• Existing attack methods for k-nearest neighbor~(kNN) based algorithms either require large perturbations or are not applicable for large k.
• To handle this problem, this paper proposes a new method called AdvKNN for evaluating the adversarial robustness of kNN-based models.
• Firstly, we propose a deep kNN block to approximate the output of kNN methods, which is differentiable thus can provide gradients for attacks to cross the decision boundary with small distortions.