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| With the development and popularization of communications technology, image and video play a more and more important role in the media, and the harm of image and video forgery is becoming more and more intense. Especially for the deeply forged video of human face, because the forgery effect of this kind of video is fine, it has strong deception and does great harm to the social credit system. The research on the detection of deep forged video has attracted the attention of scholars all over the world. The methods used can be divided into traditional methods and methods based on deep learning. Traditional methods have poor identification effect on fine forged videos or need manual participation. The method based on deep learning has the disadvantages of poor interpretability and insufficient generalization because it relies too much on data sets. In this paper, an identification model based on optical flow is proposed and tested on public data sets, which has achieved excellent results. The structure of the model proposed in this paper is simple, and the clues of identification basis are easier to understand. Experiments show that the model proposed in this paper has better interpretability and generalization. |
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| Keywords:Computer science and technology; Deepfakes; Deep Learning; Optical flow |
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