Abstract
Music adversarial attacks have garnered significant interest in the field of Music Information Retrieval (MIR). In this paper, we present Music Adversarial Inpainting Attack (MAIA), a novel adversarial attack framework that supports both white-box and black-box attack scenarios. MAIA begins with an importance analysis to identify critical audio segments, which are then targeted for modification. Utilizing generative inpainting models, these segments are reconstructed with guidance from the output of the attacked model, ensuring subtle and effective adversarial perturbations. We evaluate MAIA on multiple MIR tasks, demonstrating high attack success rates in both white-box and black-box settings while maintaining minimal perceptual distortion. Additionally, subjective listening tests confirm the high audio fidelity of the adversarial samples. Our findings highlight vulnerabilities in current MIR systems and emphasize the need for more robust and secure models.
Abstract (translated)
音乐对抗攻击在音乐信息检索（MIR）领域引起了极大的关注。本文介绍了Music Adversarial Inpainting Attack (MAIA)，这是一种新颖的对抗性攻击框架，支持白盒和黑盒攻击场景。MAIA首先进行重要性分析以识别关键音频片段，然后对这些片段进行修改。利用生成式修复模型，在被攻击模型输出的指导下重构这些片段，确保细微且有效的对抗性扰动。我们在多个MIR任务中评估了MAIA，展示了在白盒和黑盒设置下极高的攻击成功率，并保持了最小的感知失真。此外，主观听觉测试确认了对抗样本具有高音频保真度。我们的研究结果揭示了当前MIR系统中存在的漏洞，并强调需要开发更强大、更安全的模型。
URL
https://arxiv.org/abs/2509.04980
