HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Authors: Jianbo Chen, Michael I. Jordan, Martin J. Wainwright
Published: 2019-04-03 | Updated: 2020-04-28

Source: https://arxiv.org/abs/1904.02144

PDF: https://arxiv.org/pdf/1904.02144

Labels Predicted by AI

敵対的攻撃 敵対的サンプル 距離評価手法

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for ℓ₂ and ℓ_∞ similarity metrics respectively. Theoretical analysis is provided for the proposed algorithms and the gradient direction estimate. Experiments show HopSkipJumpAttack requires significantly fewer model queries than Boundary Attack. It also achieves competitive performance in attacking several widely-used defense mechanisms. (HopSkipJumpAttack was named Boundary Attack++ in a previous version of the preprint.)