提出音频深度伪造溯源新任务，区分不同伪造手法。
Audio Deepfake Verification
- 双分支结构分别捕捉音频结构与生成痕迹特征。
- 在开放场景下验证性能优于单分支模型。
- 适合需要精准识别伪造方式的安全与司法场景。
随着深度伪造技术的快速发展，仅对音频进行真伪二分类已无法满足实际需求，准确判断具体伪造方法变得至关重要。本文提出音频深度伪造验证（Audio Deepfake Verification, ADV）任务，有效克服现有伪造源追踪方法在封闭集场景下的局限，旨在实现开放集伪造源追踪。为此，提出Audity双分支架构，从音频结构和生成伪影两个维度提取伪造特征。实验表明，该双分支结构在检测与验证任务中均优于任意单分支配置，展现出卓越的综合性能。
原文摘要 · Abstract (English)
With the rapid development of deepfake technology, simply making a binary judgment of true or false on audio is no longer sufficient to meet practical needs. Accurately determining the specific deepfake method has become crucial. This paper introduces the Audio Deepfake Verification (ADV) task, effectively addressing the limitations of existing deepfake source tracing methods in closed-set scenarios, aiming to achieve open-set deepfake source tracing. Meanwhile, the Audity dual-branch architecture is proposed, extracting deepfake features from two dimensions: audio structure and generation artifacts. Experimental results show that the dual-branch Audity architecture outperforms any single-branch configuration, and it can simultaneously achieve excellent performance in both deepfake detection and verification tasks.
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