Yong Liu1,4, Zhiyu Wang2,*, Shouling Ji3, Daofu Gong1,5, Lanxin Cheng1, Ruosi Cheng1
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3499-3515, 2025, DOI:10.32604/cmc.2024.057859
- 17 February 2025
Abstract In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC… More >
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