Face forgery detection method based on image domain and frequency domain double-flow network

A technology for forgery detection and image domain, applied in the field of computer vision, can solve the problems of poor detection effect and unstable artifact detection in the image domain, and achieve the effect of good detection accuracy

Pending Publication Date: 2021-11-30
ZHEJIANG UNIV
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Problems solved by technology

[0010] This method solves the problems of poor detection effect on low-quality images of traditional face forgery detection algorithms and unstable detection of artifacts in the image domain.

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  • Face forgery detection method based on image domain and frequency domain double-flow network
  • Face forgery detection method based on image domain and frequency domain double-flow network
  • Face forgery detection method based on image domain and frequency domain double-flow network

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Embodiment Construction

[0054] A representative embodiment based on the drawings shown will now be further refined. It should be understood that the following description is not intended to limit the embodiments to one preferred embodiment. On the contrary, it is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the described embodiments as defined by the appended claims.

[0055] A face forgery detection method based on an image-domain frequency-domain dual-stream network, the detection method comprising two stages of model training and model inference;

[0056]In the model training stage, the network model is trained using a server with high computing performance, and the network parameters are optimized by reducing the network loss function until the network converges, and a dual-stream network model based on the image domain and frequency domain is obtained;

[0057] In the model inference stage, use the network model obtained in ...

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Abstract

The invention relates to the technical field of computer vision, in particular to a face forgery detection method based on an image domain and frequency domain double-flow network. The detection method comprises a model training stage and a model inference stage. In the model training stage, a server with high computing performance is used for training a network model, network parameters are optimized by reducing a network loss function until the network converges, and a double-flow network model based on an image domain and a frequency domain is obtained; in the model inference stage, the network model obtained in the model training stage is utilized to judge whether the new image is a face counterfeit image or not. Compared with a method for converting face counterfeit detection into a dichotomy problem, the method realizes hierarchical classification, and can achieve better detection precision by using diversified supervision information of counterfeit images.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a face forgery detection method based on an image-domain-frequency-domain dual-stream network. Background technique [0002] With the development of advanced face synthesis algorithms, various realistic fake faces have also been produced, and their application on social media has attracted great attention. Malicious use of forged faces will bring huge adverse effects to individuals and society. Therefore, it is very important to detect forged faces. [0003] There are many challenges in the face forgery detection task, especially the diversity of forgery algorithms, and the face forgery images circulating on the Internet are often of low quality and difficult to detect. [0004] Early studies attempted to use handcrafted features or simply modify existing neural networks. MesoNet designed a shallow neural network consisting of two initial modules and two classic convolu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/52G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/253G06F18/214
Inventor 刘勇梁雨菲王蒙蒙
Owner ZHEJIANG UNIV
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