Sparse Attack with Meta-Learning

Black-box attacks pose a significant challenge due to the restricted access to target model information, hindering the generation of impactful adversarial samples. This paper introduces a method that combines sparse attacks and meta-learning to alleviate the issue of low success rates in black-box attacks. SAM leverages the knowledge transfer capabilities inherent in meta-learning to augment the transferability of adversarial samples. The method integrates meta-learning with gradient-based attack techniques, effectively transforming the approach into a white-box attack. By aggregating multiple sampled models, SAM enhances the stability of adversarial samples. During meta-testing, simulated black-box attacks help mitigate gradient discrepancies across diverse models, consequently enhancing transferability. To further improve sparsity and preserve transferability, SAM incorporates a projection strategy that selectively sparsifies global adversarial perturbations. Experimental evaluations conducted on two image datasets substantiate SAM’s superiority in terms of both sparsity and attack success rate. Ablation experiments confirm the effectiveness of integrating meta-learning into the proposed method. SAM extends the applicability of generated adversarial samples, advancing the domain of adversarial attacks in scenarios with limited target model information. The proposed approach exhibits promise in enhancing the success rate of attacks while preserving sparsity, contributing to the broader understanding of black-box attacks and their implications.