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Manipulated Regions Localization For Partially Deepfake Audio: A Survey
Jiayi He, Jiangyan Yi, Jianhua Tao +2
With the development of audio deepfake techniques, attacks with partially deepfake audio are beginning to rise. Compared to fully deepfake, it is much harder to be identified by th…
Region-Based Optimization in Continual Learning for Audio Deepfake Detection
Yujie Chen, Jiangyan Yi, Cunhang Fan +10
Rapid advancements in speech synthesis and voice conversion bring convenience but also new security risks, creating an urgent need for effective audio deepfake detection. Although…
An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio
Siding Zeng, Jiangyan Yi, Jianhua Tao +4
When the task of locating manipulation regions in partially-fake audio (PFA) involves cross-domain datasets, the performance of deep learning models drops significantly due to the…
RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection
Yujie Chen, Jiangyan Yi, Jun Xue +7
Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments. Therefore, combining local and global feature information can ef…
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
Xiaohui Zhang, Jiangyan Yi, Chenglong Wang +3
The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective…