| 摘要: |
| 随着音频对抗攻击研究的深入, 如何在确保对抗音频隐蔽性(即与原始音频在听觉上高度相似)的同时, 提高其在不同模型之间的迁移性, 已成为研究热点之一. 提出一种能够同时提高对抗音频隐蔽性和迁移性的方法SIAttack (speak information attack). 该方法的核心思想是解耦音频中的说话人信息与内容信息, 并仅对说话人信息施加轻微扰动, 从而可以在保持内容信息不变的前提下实现对说话人识别系统的高效攻击. 在4个说话人识别模型以及3个主流商业API上的实验表明, SIAttack生成的音频在听觉上几乎无法与原始音频区分, 且能以较高的成功率误导所有测试模型, 在说话人识别模型上迁移成功率最高可达100%. |
| 关键词: 音频 隐蔽性 迁移性 对抗攻击 对抗扰动 |
| DOI：10.13328/j.cnki.jos.007574 |
| 分类号:TP309 |
| 基金项目:国家自然科学基金 (U2336208); 深圳市科技计划 (JCYJ20250604175534044) |
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| High-performance Audio Adversarial Attacks Guided by Speaker Information |
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CHEN Jia-Yuan, HUANG Wen-Hong, HUANG Fang-Jun
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School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, China
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| Abstract: |
| As the research on audio adversarial attacks advances, improving the transferability of adversarial audio across different models and ensuring its imperceptibility (that is, highly similar to the original audio in auditory perception) at the same time have become a research hotspot. This study proposes a new method called speak information attack (SIAttack) that can simultaneously improve the imperceptibility and transferability of adversarial audio. Specifically, the core idea of this method is to decouple speaker information from content information in the audio, and then apply small perturbations only to the speaker information, thereby achieving efficient attacks on the speaker recognition system under the premise of keeping the content information unchanged. The experiments on four speaker recognition models and three mainstream commercial APIs show that the audio generated by SIAttack is almost indistinguishable from the original audio, and can mislead all test models with a high success rate. Additionally, the transfer success rate on speaker recognition models can reach up to 100%. |
| Key words: audio imperceptibility transferability adversarial attack adversarial perturbation |
