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

Authors: Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
Published: 2018-12-29 | Updated: 2019-03-20

Source: https://arxiv.org/abs/1812.11377

PDF: https://arxiv.org/pdf/1812.11377

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マシン・アンラーニング 収束性分析 深層学習

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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 Hessian-aware zeroth-order algorithm called ZO-HessAware. Our theoretical result shows that 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.