A method for detecting tampered face video

Abstract

The invention discloses a face tampering video detection method, comprising: decoding the face video data into a group of continuous frame images, and intercepting the face area of ​​each frame image, and correspondingly saving it as a face picture according to the frame number; The feature extractor extracts each face picture and obtains the corresponding feature map; the feature maps of two consecutive frames are input to the inter-frame correlation classifier at the same time, and the inter-frame correlation classifier adopts the attention mechanism to combine the two frames. The feature maps are fused together for classification, and the classification result is the probability that the two input frames are tampered with. This method utilizes the information of the frame picture itself and the inter-frame relationship with its adjacent frames at the same time, which can achieve better results. At the same time, the detection is done automatically and can be applied to large-scale video platforms and social platforms.

Description

technical field [0001] The invention relates to the technical field of cyberspace security, in particular to a method for detecting tampered face video. Background technique [0002] The "face-swapping" technology based on deep neural networks is very popular. Based on these technologies, the faces in the video can be quickly replaced with other people's faces. Some criminals even tamper with the videos of politicians, stars, and celebrities, and post fake ones. information. In response to this phenomenon, there are some methods that can be used to detect whether the video has been tampered with, such as detecting eye blink frequency and detecting noise consistency. [0003] However, the detection performance of the existing methods is poor, and the accuracy of the detection results cannot be guaranteed. Especially with the rapid development of counterfeiting technology, the existing methods are difficult to meet the requirements of practical applications. Contents of the...

AI Technical Summary

Problems solved by technology

[0003] However, the detection performance of the existing methods is poor, and the accuracy of the detection results cannot be guaranteed, especially with the rapid development of counterfeiting technology, the existing methods are difficult to meet the requirements of practical applications

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