Forged face video detection method and device

A video detection and video frame technology, applied in the field of deep learning, can solve the problem of poor face recognition accuracy, and achieve the effect of improving the accuracy.

Active Publication Date: 2020-07-24
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0004] Based on this, it is necessary to address the above technical problems and provide a fake face video detect

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  • Forged face video detection method and device
  • Forged face video detection method and device
  • Forged face video detection method and device

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Embodiment Construction

[0045] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0046] In one embodiment, such as figure 1 As shown, a method for fake face video detection is provided, comprising the following steps:

[0047] Step 102, extract video frame sequence from video data.

[0048]The video data can be a video containing a human face, and the human face can be a fake human face or a real human face. The video is composed of frames of images, and the specified video frames in the video data can be extracted, and then the sequence of video frames can be obtained.

[0049] Step 104, input the video frame sequence into the pre-trained residual ...

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Abstract

The invention relates to a forged face video detection method and device. The method comprises the following steps of: extracting a video frame sequence from the video data; inputting the video framesequence into a pre-trained residual neural network, extracting residual features corresponding to the video frame sequence; and inputting the video frame sequence into a pre-trained edge texture neural network, extracting edge texture features in the video frame sequence, performing feature fusion on the residual features and the edge texture features to obtain fusion features, inputting the fusion features into a pre-trained binary convolutional neural network, and outputting a detection result whether a face is a forged face or not. By adopting the method, the accuracy of counterfeit face detection can be improved.

Description

technical field [0001] The present application relates to the field of deep learning technology, in particular to a method and device for detecting fake face videos. Background technique [0002] Deep fake face is to use algorithms such as generative confrontation network or convolutional neural network to transfer the face of the target object from the source image to the video of the imitated object. With the continuous evolution of artificial intelligence algorithms and more and more real data for deepfakes, deepfake products can achieve large-scale and automatic face swaps with little training, and their fidelity is getting higher and higher. In these fake videos, people can say things that have not been said in reality, and do things that have not been done in reality, to the extent that the fake ones are confusing the real ones, impacting people's traditional cognition of "seeing is believing". Although deep forgery technology can provide new development space for com...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/161G06V40/172G06V20/46G06N3/045G06F18/214G06F18/253
Inventor 李硕豪张军周浩蒋林承雷军
Owner NAT UNIV OF DEFENSE TECH
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