Tampered face video detection method

Abstract

The invention discloses a tampered face video detection method, which comprises the following steps of: decoding face video data into a group of continuous frame images, intercepting a face area of each frame image, and correspondingly storing the face areas as face pictures according to frame serial numbers; extracting each face picture through a feature extractor to obtain a corresponding feature map; and the feature maps of the two continuous frames are input to an inter-frame correlation classifier at the same time, the inter-frame correlation classifier adopts an attention mechanism to fuse the feature maps of the two frames together and then classifies the feature maps, and the classification result is the probability that the two input frames are tampered. According to the method, the information of the frame picture and the inter-frame relationship between the frame picture and the adjacent frame are utilized at the same time, and a good effect can be achieved. Meanwhile, detection is automatically completed, and the method can be suitable for 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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