Deep fake face video positioning method based on space-time fusion

Abstract

The invention discloses a deep fake face video positioning method based on space-time fusion. The method comprises the following steps: (1) constructing a convolutional neural network; (2) constructing a classification network fusing time domain and space domain features; (3) constructing a segmentation positioning task network; (4) constructing a reconstruction task network; (5) constructing a multi-task fusion network; (6) generating a multi-task fusion loss function; (7) generating a training set; (8) training the multi-task fusion network; (9) identifying and positioning the deeply-forged face video. According to the method, the classification network fusing the time domain and space domain features is constructed to extract the features, more complete intra-frame and inter-frame features can be extracted, and higher accuracy is obtained; meanwhile, the multi-task fusion loss function used for training the multi-task fusion network is constructed, and the accuracy of the multi-task fusion network is improved. The problem that generalization ability and function completeness are affected due to the fact that no attack category exists and tasks are simplified is solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a method for locating deeply fake human face videos based on spatio-temporal fusion in the technical field of image recognition. The invention can be applied to forgery verification of videos containing human faces and marking of forged regions. Background technique [0002] Deepfakes refer to any realistic audiovisual content produced with the help of deep learning, as well as the techniques used to create such content. With the continuous development of deep learning technology, the threshold for using deep forgery generation technology is lowered, the sensory effect is more and more realistic, the robustness is gradually improved, and the data dependence is gradually reduced. Existing deep forgery methods make the demand for the current deep forgery authentication system with high generalization ability continue to increase. [0003] The current counterfeiting...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, to provide a method for locating fake human face videos based on a spatio-temporal fusion multi-task model, which is used to solve the problems in the recognition process of fake videos caused by ignoring the temporal features of fake videos. The reduction of recognition accuracy, and the generalization ability and functional perfection of the recognition system are affected due to the neglect of unseen attack categories and task simplification.

Construct a multi-task fusion network and a multi-task fusion loss function. Since the network integrates related tasks with common characteristics for network training, it is used to solve the problem of ignoring unseen attack categories and single tasks, which will affect the generalization ability and function of the recognition system. completeness problem

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