CAU KU deep fake detection system for ADD 2023 challenge*

Abstract

The paper presents the participation of the CAU_KU team in the ADD 2023 Challenge, specifically in track 1.2 (audio fake game - detection track) and track 3 (deepfake algorithm recognition track). Various deep learning models were explored using features from the pretrained wav2vec2 network, as well as CQT, mel-spectrogram, etc. We modified the representation extraction component of the AASIST model to incorporate 2D spectrograms (wav2vec2 or CQT) and attempted different deep learning models, with model ensembling employed to create the final model. For track 1.2, our submitted ensemble model for round 1 utilized the CQT-LCNN and CQT-AASIST models. For round 2, our model used the CQT-LCNN, CQT-AASIST, and W2V2-GMM models. For track 3, we ensembled the CQT-LCNN, CQT-OFD and AASIST models. Additionally, we applied the openmax algorithm to detect unknown deepfake attacks. Our best submission achieved 23.44% and 21.26% on round 1 and 2 of track 1.2, respectively, and ranked 3rd in track 1.2. © 2023 CEUR-WS. All rights reserved.