Wenkai Qin1, Tianliang Lu1,*, Lu Zhang2, Shufan Peng1, Da Wan1
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 467-490, 2023, DOI:10.32604/cmc.2023.042417
Abstract With the rapid development of deepfake technology, the authenticity of various types of fake synthetic content is increasing rapidly, which brings potential security threats to people's daily life and social stability. Currently, most algorithms define deepfake detection as a binary classification problem, i.e., global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false. However, the differences between real and fake samples are often subtle and local, and such global feature-based detection algorithms are not optimal in efficiency and accuracy. To this end, to enhance the extraction of forgery details… More >
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