Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack
This work addresses the challenge of efficient adversarial attacks in machine learning security, offering an incremental improvement by incorporating second-order information into zeroth-order optimization.
The paper tackles the problem of black-box adversarial attacks on neural network image classifiers by proposing a Hessian-aware zeroth-order optimization algorithm, ZO-HessAware, which achieves improved success rates with lower query complexity compared to existing methods.
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.