  Fooling deep neural networks (DNNs) with the black-box optimization has
become a popular adversarial attack fashion, as the structural prior knowledge
of DNNs is always unknown. Nevertheless, 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. Instead of
attacking the entire image, reducing the perturbed pixels with the attention
mechanism can help to avoid the notorious curse of dimensionality and thereby
improves the performance of attacking. Secondly, a large-scale multiobjective
evolutionary algorithm is employed to traverse the reduced pixels in the
salient region. Benefiting from its characteristics, the generated AEs have the
potential to fool target DNNs while being imperceptible by the human vision.
Extensive experimental results have verified the effectiveness of the proposed
LMOA on the ImageNet dataset. More importantly, it is more competitive to
generate high-resolution AEs with better visual quality compared with the
existing black-box adversarial attacks.
