Singing voice synthesis and singing voice conversion have significantly
advanced, revolutionizing musical experiences. However, the rise of "Deepfake
Songs" generated by these technologies raises concerns about authenticity.
Unlike Audio DeepFake Detection (ADD), the field of song deepfake detection
lacks specialized datasets or methods for song authenticity verification. In
this paper, we initially construct a Chinese Fake Song Detection (FSD) dataset
to investigate the field of song deepfake detection. The fake songs in the FSD
dataset are generated by five state-of-the-art singing voice synthesis and
singing voice conversion methods. Our initial experiments on FSD revealed the
ineffectiveness of existing speech-trained ADD models for the task of Song
DeepFake Detection. Thus, we employ the FSD dataset for the training of ADD
models. We subsequently evaluate these models under two scenarios: one with the
original songs and another with separated vocal tracks. Experiment results show
that song-trained ADD models exhibit an approximate 38.58% reduction in average
equal error rate compared to speech-trained ADD models on the FSD test set.

利用中国伪造歌曲检测（FSD）数据集，通过训练音频伪造检测（ADD）模型，实现对歌曲伪造的验证，并发现歌曲专用 ADD 模型在 FSD 测试集中平均等误差率较训练于语音的 ADD 模型降低了约 38.58%。