Abstract
With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library.
Highlights
In order to attenuate the harm caused by the deep face forgery technology, researchers carried out in-depth exploration face forgery video detection technology and put forward the idea of detection from multiple perspectives such as the space domain, time domain and frequency domain
DFD database contains 1089 real videos and 9204 face forgery videos, which are divided into 3 different compression levels: synthetic compression rate 0 (C0), synthetic compression rate 23 (C23) and synthetic compression rate 40 (C40)
There are 1000 face forgery videos synthesized by Deepfake tampering, which are divided into 3 different compression degrees: synthetic compression rate 0 (C0), synthetic compression rate 23 (C23) and synthetic compression rate 40 (C40)
Summary
The human face usually provides important identity information; many related studies were carried out, including face detection and recognition in 2D and 3D spaces [1–4]. In order to attenuate the harm caused by the deep face forgery technology, researchers carried out in-depth exploration face forgery video detection technology and put forward the idea of detection from multiple perspectives such as the space domain, time domain and frequency domain. These methods achieved satisfactory detection performance on some data sets. Deep forgery is an image synthesis technology based on deep learning It mainly uses a generative adversarial network, deep convolutional neural network and automatic encoder to forge a set of primitive faces and target faces as training data. Based on a similar idea, researchers proposed and developed more face replacement methods and achieved better replacement results
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