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Manipulated Regions Localization For Partially Deepfake Audio: A Survey
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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 the detector due to the partially cryptic manipulation, resulting in higher security risks. Although some studies have been launched, there is no comprehensive review to systematically introduce the current situations and development trends for addressing this issue. Thus, in this survey, we are the first to outline a systematic introduction for partially deepfake audio manipulated region localization tasks, including the fundamentals, branches of existing methods, current limitations and potential trends, providing a revealing insight into this scope.
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Localizing Audio-Visual Deepfakes via Hierarchical Boundary Modeling
HBMNet, a hierarchical boundary-modeling network, outperforms BA-TFD and UMMAFormer at localizing partially manipulated audio-visual segments on a subset of AV-Deepfake-1M.
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