International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Audio Deepfake Detection using Deep Learning Techniques |
|---|---|
| ABSTRACT | With the rapid development of speech synthesis and voice conversion technologies, Audio Deepfake has become a se- rious threat to the Automatic Speaker Verification (ASV) sys- tem. 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 Atten- tive classifier for audio deepfake detection. Our method ex- ploits the WavLM model to extract features that are more con- ducive to spoofing detection for the first time. Then, we pro- pose 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. |
| AUTHOR | PROF. DR. CHETANA PRAKASH, SAGAR K, SYED KHALIL Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davangere, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312149 |
| pdf/149_Audio Deepfake Detection using Deep Learning Techniques.pdf | |
| KEYWORDS | |
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