  Detecting singing voice deepfakes, or SingFake, involves determining the
authenticity and copyright of a singing voice. Existing models for speech
deepfake detection have struggled to adapt to unseen attacks in this unique
singing voice domain of human vocalization. To bridge the gap, we present a
groundbreaking SingGraph model. The model synergizes the capabilities of the
MERT acoustic music understanding model for pitch and rhythm analysis with the
wav2vec2.0 model for linguistic analysis of lyrics. Additionally, we advocate
for using RawBoost and beat matching techniques grounded in music domain
knowledge for singing voice augmentation, thereby enhancing SingFake detection
performance. Our proposed method achieves new state-of-the-art (SOTA) results
within the SingFake dataset, surpassing the previous SOTA model across three
distinct scenarios: it improves EER relatively for seen singers by 13.2%, for
unseen singers by 24.3%, and unseen singers using different codecs by 37.1%.
