Face detection method, and training method and device of face detection model

Abstract

The invention discloses a face detection method for protecting user privacy and improving information security. The method can be applied to the field of artificial intelligence, and comprises the following steps: acquiring a face image sequence; obtaining a noise image sequence according to the face image sequence; obtaining a face feature map sequence of the face image sequence through a first feature extraction network of a face detection model; obtaining a noise feature map sequence of the noise image sequence through a second feature extraction network of the face detection model; based on the face feature map sequence and the noise feature map sequence, obtaining a classification probability value corresponding to the face image through a full connection layer of the face detection model; and determining a detection result according to the classification probability value. The invention further provides a training method and device of the face detection model. By paying attentionto semantic information of the human face image sequence and noise information of the noise image sequence at the same time, artifacts generated by fake human faces can be effectively mined, and theaccuracy of human face detection is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a face detection method, a face detection model training method and a device. Background technique [0002] With the development of artificial intelligence (AI) technologies such as generative confrontation networks, deep face generation technology and its applications are becoming more and more mature. People can quickly realize face generation, face editing and face replacement through neural networks. Face generation technology has promoted the emerging development of the entertainment and cultural exchange industries, but it also poses a huge potential threat to face security. [0003] At present, a face detection model based on forgery traces of faces is provided, and the face detection model mainly judges specific forgery traces of false content. Because there will be unnatural blinking in the generated face-changing images or videos, the face det...

AI Technical Summary

Problems solved by technology

[0004] However, face detection models based on face forgery traces only perform well for face-swapped videos containing specific forgery traces, but lack generalization

If the training of blinking face images is added to the face forgery algorithm, the generated face-changing images or videos will be closer to the natural blinking phenomenon, resulting in lower detection accuracy of the face detection model

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