Face depth tampered image detection method based on multi-scale depth feature fusion

Abstract

The invention belongs to the field of face recognition, deep learning and image forensics, particularly relates to a face deep tampered image detection method, system and device based on multi-scale depth feature fusion, and aims to solve the problem of low detection accuracy of a face deep tampered image detection method. The method of the system comprises the following steps: acquiring a to-be-detected face image as an input image; performing normalization processing on the input image, and obtaining a detection result through a pre-trained face detection model; the face detection model is constructed based on a convolutional neural network, and a convolutional layer of the face detection model is composed of a deep convolutional network and a hole convolutional network. According to theinvention, the detection rate of the human face deep tampered image is improved.

Description

technical field [0001] The invention belongs to the fields of face recognition, deep learning and image forensics, and specifically relates to a detection method, system and device for deeply tampered images of faces based on fusion of multi-scale depth features. Background technique [0002] With the vigorous development of artificial intelligence technology and automation technology in recent years, digital image tampering technology has reached a new height and level. Face deep forgery technology was first proposed in 2017. It is an emerging technology that uses deep learning to tamper with multimedia images or video content. Different from traditional manual image tampering methods, such as manually editing the content of face images through image editing software such as Photoshop, GIMP, etc., face depth tampering images use massive face images or video data to train face deep forgery generation models, The generation effect is enhanced through model iterative update a...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above problems in the prior art, that is, in order to solve the problem of low detection accuracy of the existing face depth tampering image detection method, the first aspect of the present invention proposes a face depth detection method based on multi-scale depth feature fusion A detection method for tampering with an image, the method comprising:

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