Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent


  • Pu Zhao Northeastern University
  • Pin-yu Chen IBM Research
  • Siyue Wang Northeastern University
  • Xue Lin Northeastern University



Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability. Various adversarial attacks are proposed to sabotage the learning performance of DNN models. Among those, the black-box adversarial attack methods have received special attentions owing to their practicality and simplicity. Black-box attacks usually prefer less queries in order to maintain stealthy and low costs. However, most of the current black-box attack methods adopt the first-order gradient descent method, which may come with certain deficiencies such as relatively slow convergence and high sensitivity to hyper-parameter settings. In this paper, we propose a zeroth-order natural gradient descent (ZO-NGD) method to design the adversarial attacks, which incorporates the zeroth-order gradient estimation technique catering to the black-box attack scenario and the second-order natural gradient descent to achieve higher query efficiency. The empirical evaluations on image classification datasets demonstrate that ZO-NGD can obtain significantly lower model query complexities compared with state-of-the-art attack methods.




How to Cite

Zhao, P., Chen, P.- yu, Wang, S., & Lin, X. (2020). Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6909-6916.



AAAI Technical Track: Machine Learning