  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.
