Adversarial attack algorithm for object detection based on improved differential evolution

Jinhui Ye; Yongping Wang; Xiaolin Zhang; Li Xu; Ruizhi Ni

doi:10.1117/12.2653117

20 October 2022 Adversarial attack algorithm for object detection based on improved differential evolution

Jinhui Ye, Yongping Wang, Xiaolin Zhang, Li Xu, Ruizhi Ni

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123502M (2022) https://doi.org/10.1117/12.2653117
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

With the wide application of deep learning object detection model, its internal security vulnerability is also highlighted. In this paper, a black box adversarial attack algorithm SAD-DE based on improved differential evolution is proposed to reveal the possible security risks of the object detection model. Taking full advantage of the high optimization efficiency and simple parameter setting of differential evolution algorithm, multi mutation strategy is adopted, and the mutation rate and crossover rate are adaptively improved to effectively improve the optimization efficiency. In this paper, two public data sets are randomly exampled as test sets to counter attacks on YOLOv3 and Fast R-CNN respectively. Experimental results show that this method achieves a high fooling rate while maintaining a low anti disturbance, and achieves a maximum improvement of 33% compared with other black box attack algorithms.

Citation Download Citation

Jinhui Ye, Yongping Wang, Xiaolin Zhang, Li Xu, and Ruizhi Ni "Adversarial attack algorithm for object detection based on improved differential evolution", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123502M (20 October 2022); https://doi.org/10.1117/12.2653117

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KEYWORDS

Detection and tracking algorithms

Chromium

Target detection

Image classification

Data modeling

Evolutionary algorithms

Visual process modeling

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