Abstract: Deep neural networks have achieved widespread application in the field of imagerecognition,however,their complex structures make them vulnerable to adversarial attacks.Constructing adversarial examples that are imperceptible to the human eye is crucial for evaluating the security of these networks.Existing adversarial example generation methods for images typically involve small perturbations to the original samples,constrained by l_p-norms.This simplistic approach treats all pixels equally,applying uniform constraints to the allowable perturbations at each pixel,which limits the flexibility of adversarial example generation and makes the perturbations more detectable to the human eye.In practical applications,human visual sensitivity varies across different colors and image regions.To address this issue,this paper proposes an adaptive perturbation scheme based on perceptual sensitivity,where different perturbation constraints are applied to different pixels,thereby enhancing the robustness of the adversarial examples.Specifically,this method employs spectral analysis to divide the image into high-frequency and low-frequency regions and applies novel spatial constraints to regulate perturbations.Larger perturbations are introduced in regions less sensitive to high-frequency changes,improving adversarial effectiveness.Extensive experiments conducted on the ImageNet-1K and CIFAR-10 datasets demonstrate that the proposed adversarial example generation strategy can be coupled with various attack me-thods,significantly enhancing adversarial performance while ensuring imperceptibility.

Key words: Deep neural networks,Adversarial examples,High-frequency,Perturbations,Robustness