HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
Jianbo Chen, Michael I. Jordan, Martin J. Wainwright

TL;DR
HopSkipJumpAttack is a query-efficient decision-based adversarial attack method that estimates gradient directions using binary outputs, requiring fewer queries and effectively bypassing defenses.
Contribution
It introduces a novel gradient estimation technique for decision-based attacks, improving query efficiency and attack success rates against defended models.
Findings
Requires fewer queries than Boundary Attack
Achieves competitive attack success rates
Effective against various defense mechanisms
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 and 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.)
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Domain Adaptation and Few-Shot Learning
