An efficient general black-box adversarial attack approach based on multi-objective optimization for high dimensional images. (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Chunkai; Guo, Xin; Deng, Yepeng; Wang, Xuan; Han, Peiyi; Liu, Chuanyi; Zhang, Hanyu
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Adversarial examples -- Black-box attack -- Multi-objective optimization -- High dimension; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107402
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In black-box scenarios, the adversarial attack algorithms based on iterative optimization perform best. But it may give a false sense of model robustness due to the design of inefficient queries. The adversarial attack algorithm based on multi-objective evolution optimization has been proven to be very effective for the low-dimensional images. However, when the attack space dramatically increases for the high-dimensional color images, the evolutionary efficiency is limited, and it needs more inefficient queries to generate adversarial examples. In this paper, we propose an efficient black-box adversarial attack approach for high dimensional images based on multi-objective optimization (MOO-HD), which includes some novel strategies to solve the above problems. We also propose the strategy of "The transformation of the pixel block with a random step size" to reduce the attack space. The experimental results on three image datasets with different dimensions show that our algorithm can achieve a higher success rate with fewer queries.