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    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/149882


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    題名: Lip Sync Matters: A Novel Multimodal Forgery Detector
    作者: 彭彥璁
    Peng, Yan-Tsung;Shahzad, Sahibzada Adil;Hashmi, Ammarah;Khan, Sarwar;Tsao, Yu;Wang, Hsin-Min
    貢獻者: 資訊系
    日期: 2022-11
    上傳時間: 2024-02-16 15:36:53 (UTC+8)
    摘要: Deepfake technology has advanced a lot, but it is a double-sided sword for the community. One can use it for beneficial purposes, such as restoring vintage content in old movies, or for nefarious purposes, such as creating fake footage to manipulate the public and distribute non-consensual pornography. A lot of work has been done to combat its improper use by detecting fake footage with good performance thanks to the availability of numerous public datasets and unimodal deep learning-based models. However, these methods are insufficient to detect multimodal manipulations, such as both visual and acoustic. This work proposes a novel lip-reading-based multi-modal Deepfake detection method called “Lip Sync Matters.” It targets high-level semantic features to exploit the mismatch between the lip sequence extracted from the video and the synthetic lip sequence generated from the audio by the Wav2lip model to detect forged videos. Experimental results show that the proposed method outperforms several existing unimodal, ensemble, and multimodal methods on the publicly available multimodal FakeAVCeleb dataset.
    關聯: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE
    資料類型: conference
    DOI 連結: https://doi.org/10.23919/APSIPAASC55919.2022.9980296
    DOI: 10.23919/APSIPAASC55919.2022.9980296
    顯示於類別:[資訊科學系] 會議論文

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