WaveAttn-AdvGAN: Image adversarial example generation method based on discrete wavelet transform and global grouped coordinate attention

Deep neural networks (DNNs) excel at image classification tasks, but their vulnerability to adversarial examples raise serious security concerns. Although existing adversarial attack methods demonstrate certain threat levels in black-box scenarios, they still exhibit limitations in frequency-domain feature utilization, fine-grained attention mechanisms, and multi-objective optimization coordination. This work proposes WaveAttn-AdvGAN, a framework that integrates Discrete Wavelet Transform (DWT) with Global Grouped Coordinate Attention (GGCA) through bidirectional collaborative optimization. The GGCA module employs group pooling and dual-dimensional attention mechanisms to precisely localize critical regions and optimize perturbation distribution. Concurrently, DWT decomposes images to identify high-frequency sensitive subbands, where Gaussian noise injection rectify gradient directions to improve transferability. A multi-objective optimization function combining adversarial loss, attention loss, and perturbation constraints achieves balanced attack efficacy, imperceptibility, and transferability. Experimental results demonstrate a 98.94 % white-box Attack Success Rate (ASR) and 2.39 % average improvement in black-box transfer attacks, with Structural Similarity Index Measure (SSIM), Frechet Inception Distance (FID), and Low-Frequency Distortion (LF) metrics confirming the superior visual stealth of the generated adversarial examples.