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Copy-paste fake image evidence obtaining method based on end-to-end deep neural network

A technology of deep neural network and neural network model, which is applied in neural learning methods, biological neural network models, image enhancement, etc., and can solve problems such as inability to identify forged targets

Inactive Publication Date: 2021-02-26
GUANGDONG MECHANICAL & ELECTRICAL COLLEGE
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

However, this method relies entirely on the DNN structure, resulting in the inability to recognize fake objects of many categories, especially the untrained object categories

Method used

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  • Copy-paste fake image evidence obtaining method based on end-to-end deep neural network
  • Copy-paste fake image evidence obtaining method based on end-to-end deep neural network
  • Copy-paste fake image evidence obtaining method based on end-to-end deep neural network

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Embodiment Construction

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0062] See figure 1 , in one embodiment of the present invention, based on the end-to-end Dense-InceptionNet neural network, the image copy-paste forgery forensics method includes:

[0063] Step 1: Construct an end-to-end Dense-InceptionNet neural network model.

[0064] The end-to-end Dense-InceptionNet neural network model model is mainly divided into three modules: Dense-InceptionNet pyramid feature extraction (Pyramid Feature Extractor, PFE) module, feature correlation matching (Feature C...

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Abstract

The invention discloses a copy-paste forged image evidence obtaining method based on an end-to-end deep neural network, and the method is characterized in that the method comprises the steps: 1, constructing an end-to-end Dense-InceptionNet neural network model which comprises a Dense-InceptionNet pyramid feature extraction module, a feature correlation matching module, a hierarchical post-processing module and a step 2, and training the end-to-end Dense-InceptionNet neural network model. Compared with the prior art, the model provided by the invention mainly takes feature correlation as a learning object, searches a suspected counterfeit area through a feature matching clue, and can accurately position the counterfeit area at a pixel level.

Description

technical field [0001] The invention relates to the field of image forgery evidence collection, in particular to a copy-paste forgery image evidence collection method based on an end-to-end deep neural network. Background technique [0002] With the advancement of image processing technology and the popularization of easy-to-use image editing software, modification of digital images has become easy and common. People can use image editing software to modify and process the image content, so that the image can convey information according to the wishes of the modifier. Although most image editing software users modify images for personal interest. However, lawbreakers will maliciously forge images and disseminate false information to the public in order to achieve their criminal goals. Among many image forgery techniques, copy-paste forgery has gradually become the most commonly used forgery method because of its simple technology and realistic effect. Image copy-paste for...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/20016G06T2207/20081G06T2207/20084G06N3/045
Inventor 钟君柳杨继翔甘艳芬黄练陆镔彬敖浩朗
Owner GUANGDONG MECHANICAL & ELECTRICAL COLLEGE
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