论文标题
对综合语音检测的对抗性攻击的可转移性
Transferability of Adversarial Attacks on Synthetic Speech Detection
论文作者
论文摘要
综合语音检测是音频安全性最重要的研究问题之一。同时，深度神经网络容易受到对抗性攻击的影响。因此，我们建立了一个全面的基准测试，以评估对综合语音检测任务的对抗性攻击的可转移性。具体来说，我们尝试研究：1）不同特征之间对抗性攻击的可传递性。 2）特征的不同提取超参数对对抗攻击的转移性的影响。 3）剪辑或自助攻操作对对抗攻击的转移性的影响。通过执行这些分析，我们总结了合成语音探测器的弱点以及对抗攻击的可转移性行为，这些行为为将来的研究提供了见解。更多详细信息可以在https://gitee.com/djc_qrick/attack-transferability-on-on-synthetic-detection中找到。
Synthetic speech detection is one of the most important research problems in audio security. Meanwhile, deep neural networks are vulnerable to adversarial attacks. Therefore, we establish a comprehensive benchmark to evaluate the transferability of adversarial attacks on the synthetic speech detection task. Specifically, we attempt to investigate: 1) The transferability of adversarial attacks between different features. 2) The influence of varying extraction hyperparameters of features on the transferability of adversarial attacks. 3) The effect of clipping or self-padding operation on the transferability of adversarial attacks. By performing these analyses, we summarise the weaknesses of synthetic speech detectors and the transferability behaviours of adversarial attacks, which provide insights for future research. More details can be found at https://gitee.com/djc_QRICK/Attack-Transferability-On-Synthetic-Detection.
