标题： 多级SSL特征门控用于音频深度伪造检测 显示英文标题

标题： Multi-level SSL Feature Gating for Audio Deepfake Detection

摘要： 最近生成式人工智能的进展，尤其是在语音合成方面，使得生成高度自然的合成语音成为可能，这种语音非常接近人类的声音。 尽管这些创新在辅助技术等应用中具有前景，但也带来了重大风险，包括被滥用于欺诈活动、身份盗窃和安全威胁。 目前针对欺骗检测的对策研究仍受限于对未见过的深度伪造攻击和语言的泛化能力。 为了解决这个问题，我们提出了一种门控机制，从语音基础模型XLS-R中提取相关特征作为前端特征提取器。 对于下游后端分类器，我们采用多核门控卷积（MultiConv）来捕捉局部和全局的语音伪影。 此外，我们引入中心核对齐（CKA）作为相似性度量，以在不同MultiConv层之间强制学习特征的多样性。 通过将CKA与我们的门控机制结合，我们假设每个组件都有助于提高对不同合成语音模式的学习效果。 实验结果表明，我们的方法在领域内基准测试中达到了最先进性能，并且在领域外数据集上具有强大的泛化能力，包括多语言语音样本。 这突显了其作为检测不断演变的语音深度伪造威胁的多功能解决方案的潜力。 显示英文摘要

摘要： Recent advancements in generative AI, particularly in speech synthesis, have enabled the generation of highly natural-sounding synthetic speech that closely mimics human voices. While these innovations hold promise for applications like assistive technologies, they also pose significant risks, including misuse for fraudulent activities, identity theft, and security threats. Current research on spoofing detection countermeasures remains limited by generalization to unseen deepfake attacks and languages. To address this, we propose a gating mechanism extracting relevant feature from the speech foundation XLS-R model as a front-end feature extractor. For downstream back-end classifier, we employ Multi-kernel gated Convolution (MultiConv) to capture both local and global speech artifacts. Additionally, we introduce Centered Kernel Alignment (CKA) as a similarity metric to enforce diversity in learned features across different MultiConv layers. By integrating CKA with our gating mechanism, we hypothesize that each component helps improving the learning of distinct synthetic speech patterns. Experimental results demonstrate that our approach achieves state-of-the-art performance on in-domain benchmarks while generalizing robustly to out-of-domain datasets, including multilingual speech samples. This underscores its potential as a versatile solution for detecting evolving speech deepfake threats.