利用情绪特征提升音频深度伪造检测效果，显著增强泛化能力。
Emoanti: audio anti-deepfake with refined emotion-guided representations
- 基于情绪感知的Wav2Vec2模型提取情感引导表征
- 在ASVspoof2019LA与2021LA上达到最先进性能
- 适合关注音频安全与伪造检测的研究者
音频深度伪造技术日益复杂，现有检测方法多依赖低层声学特征或预训练语音表征，常忽略高层情绪线索。本文提出EmoAnti系统，通过在情感识别任务上微调预训练的Wav2Vec2（W2V2）模型，生成情感引导表征，并设计基于卷积层与残差连接的专用特征提取器，从Transformer输出中有效捕捉并优化情感特征。实验表明，该架构在ASVspoof2019LA和ASVspoof2021LA基准上均达到当前最优表现，并在ASVspoof2021DF数据集上展现出强泛化能力。代码已公开于匿名GitHub仓库。
原文摘要 · Abstract (English)
Audio deepfake is so sophisticated that the lack of effective detection methods is fatal. While most detection systems primarily rely on low-level acoustic features or pretrained speech representations, they frequently neglect high-level emotional cues, which can offer complementary and potentially anti-deepfake information to enhance generalization. In this work, we propose a novel audio anti-deepfake system that utilizes emotional features (EmoAnti) by exploiting a pretrained Wav2Vec2 (W2V2) model fine-tuned on emotion recognition tasks, which derives emotion-guided representations, then designing a dedicated feature extractor based on convolutional layers with residual connections to effectively capture and refine emotional characteristics from the transformer layers outputs. Experimental results show that our proposed architecture achieves state-of-the-art performance on both the ASVspoof2019LA and ASVspoof2021LA benchmarks, and demonstrates strong generalization on the ASVspoof2021DF dataset. Our proposed approach's code is available at Anonymous GitHub1.
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