Betray Oneself: A Novel Audio DeepFake Detection Model via
  Mono-to-Stereo Conversion
Rui Liu Jinhua Zhang Guanglai Gao Haizhou Li
Audio Deepfake Detection (ADD) aims to detect the fake audio generated by
text-to-speech (TTS), voice conversion (VC) and replay, etc., which is an
emerging topic. Traditionally we take the mono signal as input and focus on
robust feature extraction and effective classifier design. However, the
dual-channel stereo information in the audio signal also includes important
cues for deepfake, which has not been studied in the prior work. In this paper,
we propose a novel ADD model, termed as M2S-ADD, that attempts to discover
audio authenticity cues during the mono-to-stereo conversion process. We first
projects the mono to a stereo signal using a pretrained stereo synthesizer,
then employs a dual-branch neural architecture to process the left and right
channel signals, respectively. In this way, we effectively reveal the artifacts
in the fake audio, thus improve the ADD performance. The experiments on the
ASVspoof2019 database show that M2S-ADD outperforms all baselines that input
mono. We release the source code at \url{https://github.com/AI-S2-Lab/M2S-ADD}.