Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization

Recent black-box adversarial attacks may struggle to balance their attack ability and visual quality of the generated adversarial examples (AEs) in tackling high-resolution images. In this paper, We propose an attention-guided black-box adversarial attack based on the large-scale multiobjective evolutionary optimization, termed as LMOA. By considering the spatial semantic information of images, we firstly take advantage of the attention map to determine the perturbed pixels. Then, a large-scale multiobjective evolutionary algorithm is employed to traverse the reduced pixels in the salient region. Extensive experimental results have verified the effectiveness of the proposed LMOA on the ImageNet dataset.