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arXiv · 1812.11377

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack

Haishan Ye·Zhichao Huang·Cong Fang·Chris Junchi Li·Tong Zhang
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Abstract

Zeroth-order optimization is an important research topic in machine learning. In recent years, it has become a key tool in black-box adversarial attack to neural network based image classifiers. However, existing zeroth-order optimization algorithms rarely extract second-order information of the model function. In this paper, we utilize the second-order information of the objective function and propose a novel \textit{Hessian-aware zeroth-order algorithm} called \texttt{ZO-HessAware}. Our theoretical result shows that \texttt{ZO-HessAware} has an improved zeroth-order convergence rate and query complexity under structured Hessian approximation, where we propose a few approximation methods for estimating Hessian. Our empirical studies on the black-box adversarial attack problem validate that our algorithm can achieve improved success rates with a lower query complexity.

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Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang. 2018-12-29. Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. https://arxiv.org/abs/1812.11377

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