Deepfake detection method based on video frame sequence prediction

Abstract

The invention relates to a deepfake detection method based on video frame sequence prediction, and aims to improve the attention of a time sequence model on time sequence characteristics. According to a technical scheme in the invention, the deepfake detection method based on the video frame sequence prediction is characterized by comprising the steps that a suspicious video is input into a trained time sequence model, features of the suspicious video are extracted through the time sequence model, the features are input into a true and false classifier, and the true and false classifier outputs the true and false probability of the suspicious video. The training of a time sequence model comprises the following steps: randomly disorganizing original continuous video frames of video clips, and recording a disorganizing mode; inputting the disordered video frames into the time sequence model to extract features, and sending the features into a frame sequence classifier and the true and false classifier at the same time; and calculating frame sequence prediction loss between the result of the frame sequence classifier and the disorganizing mode, and calculating true and false classification loss between the result of the true and false classifier and true and false labels of the video clips. The method is suitable for the fields of machine learning and computer vision.

Description

technical field [0001] The invention relates to a deep forgery detection method based on video frame sequence prediction. Applicable to the fields of machine learning and computer vision. Background technique [0002] In recent years, deep learning technology has continued to develop and has been widely used in the field of computer vision. On the one hand, deep learning technology has led a new wave of artificial intelligence, but on the other hand, a series of security issues caused by deep learning have also attracted more and more attention. At present, image and video recognition technologies based on deep learning are widely used in all aspects of people's lives, such as intelligent supervision of network content, automatic video monitoring and analysis, access control systems based on face recognition, and facial recognition payment. In these key application fields, the reliability and security of information and data should be paid attention to and guaranteed. [...

AI Technical Summary

Problems solved by technology

In this case, the time series model trained is often unable to model the abnormal characteristics of time series well, and cannot be well extended to fake videos or new types of fakes in real scenes.

This causes a large performance loss when the model trained in the public data set is used in the real scene

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