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.)
@article{arxiv.1904.02144,
title = {HopSkipJumpAttack: A Query-Efficient Decision-Based Attack},
author = {Jianbo Chen and Michael I. Jordan and Martin J. Wainwright},
journal= {arXiv preprint arXiv:1904.02144},
year = {2020}
}