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Adversarial attack algorithm for object detection based on improved differential evolution

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.