With the proliferation of deepfake audio, there is an urgent need to investigate their attribution. Current source tracing methods can effectively distinguish in-distribution (ID) categories. However, the rapid evolution of deepfake algorithms poses a critical challenge in the accurate identification of out-of-distribution (OOD) novel deepfake algorithms. In this paper, we propose Real Emphasis and Fake Dispersion (REFD) strategy for audio deepfake algorithm recognition, demonstrating its effectiveness in discriminating ID samples while identifying OOD samples. For effective OOD detection, we first explore current post-hoc OOD methods and propose NSD, a novel OOD approach in identifying novel deepfake algorithms through the similarity consideration of both feature and logits scores. REFD achieves 86.83% F1-score as a single system in Audio Deepfake Detection Challenge 2023 Track3, showcasing its state-of-the-art performance.

翻译：随着深度伪造音频的广泛传播，对其来源进行归因分析的需求日益迫切。现有的溯源方法能有效区分分布内类别。然而，深度伪造算法的快速演进对准确识别分布外新型伪造算法提出了严峻挑战。本文提出用于音频深度伪造算法识别的真实强调与伪造离散策略，验证了其在区分分布内样本的同时识别分布外样本的有效性。为实现有效的分布外检测，我们首先探索了现有后验分布外检测方法，并提出NSD——一种通过综合考量特征与逻辑分数相似性来识别新型伪造算法的创新分布外检测方法。在2023年音频深度伪造检测挑战赛第三赛道中，本策略作为独立系统取得86.83%的F1分数，展现了其前沿性能。