  With the rapid development of speech synthesis and voice conversion
technologies, Audio Deepfake has become a serious threat to the Automatic
Speaker Verification (ASV) system. Numerous countermeasures are proposed to
detect this type of attack. In this paper, we report our efforts to combine the
self-supervised WavLM model and Multi-Fusion Attentive classifier for audio
deepfake detection. Our method exploits the WavLM model to extract features
that are more conducive to spoofing detection for the first time. Then, we
propose a novel Multi-Fusion Attentive (MFA) classifier based on the Attentive
Statistics Pooling (ASP) layer. The MFA captures the complementary information
of audio features at both time and layer levels. Experiments demonstrate that
our methods achieve state-of-the-art results on the ASVspoof 2021 DF set and
provide competitive results on the ASVspoof 2019 and 2021 LA set.
