Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion

Adversarial attacks have become a significant challenge in the security of ma-chine learning models, particularly in the context of black-box defense strategies. Existing methods for enhancing adversarial transferability primarily focus on the spatial domain. This paper presents Frequency-Space Attack FSA , a new adversarial attack framework that effectively integrates frequency-domain and spatial-domain transformations. FSA combines two key techniques: 1 High-Frequency Augmentation, which applies Fourier transform with frequency selective amplification to diversify inputs and emphasize the critical role of high-frequency components in adversarial attacks, and 2 Hierarchical-Gradient Fusion, which merges multi-scale gradient decomposition and fusion to capture both global structures and fine-grained details, resulting in smoother perturbations. Our experiment demonstrates that FSA consistently outperforms state-of-the-art methods across various black-box models. Notably, our pro-posed FSA achieves an average attack success rate increase of 23.6 compared with BSR CVPR 2024 on eight black-box defense models.