Generalize Audio Deepfake Algorithm Recognition via Attribution Enhancement

The development of voice cloning techniques has made forgery audios indistinguishable, posing an urgency to trace their sources. Many existing works focus on improving identification accuracy for audio deepfake algorithm recognition. However, most methods ignore the impact of complex information in audio signals on attribution. In this paper, we propose an audio deepfake attribution enhancement (ADAE) strategy, which aims to magnify the fingerprints of generation styles by removing the speaker information. This is achieved through an information disentangle block with an extra speaker encoder. In addition, we propose the FakeSource dataset, a novel audio deepfake algorithm recognition dataset that contains 25 different voice cloning algorithms, to address the constraint of data scarcity. Experiments on the FakeSource dataset demonstrate that ADAE improves the performance of unseen algorithm detection. We also assess ADAE on a more challenging training-free task which shows competitive performance.