Image steganalysis method and system based on convolutional neural network

A convolutional neural network and steganalysis technology, applied in the field of image steganalysis method and system based on convolutional neural network, to achieve the effect of good detection ability

Active Publication Date: 2018-12-07
SUN YAT SEN UNIV
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Problems solved by technology

[0005] In order to solve the defects of the existing JPEG steganalysis method based on convolutional neural network, the present invention selects a group of Gabor filters with multi-directional and multi-sc

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  • Image steganalysis method and system based on convolutional neural network
  • Image steganalysis method and system based on convolutional neural network
  • Image steganalysis method and system based on convolutional neural network

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Embodiment Construction

[0040] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0041] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0042] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0043] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] Such as figure 1 As shown, a JPEG image steganalysis method based on convolutional neural network, including the following steps:

[0045] S1: Generate the image database required for the experiment. Since the present invention is a research in the field of steganalysis, the image library has selected the BOSSbase v1.01 database that is often used in the field of steganography and ste...

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Abstract

The invention discloses an image steganalysis method and system based on the convolutional neural network. The system comprises an image preprocessing part, a feature extraction part and a feature classification module. In the image preprocessing part, a group of Gabor filters with multiple directions and multi-dimension parameters is selected by experiments, and an image residual error with highsignal to noise ratio is obtained by input image convolution; and in the feature extraction part, a rapid connection structure is used to connect output of a shallow layer with a behind layer directlyto reduce a fitting phenomenon. The steganalysis method based on the convolutional neural network does not need a lot of knowledge in the fields of steganography and steganalysis, feature extractionand feature classification form a combined optimization process, design is simple, and enforcement is easy; the scale and direction properties of the Gabor filters can be used to help extract the image residual error from the network more effectively; and the method and system have better detection effects for adaptive steganography algorithms of J-UNIWARD and UED contents.

Description

technical field [0001] The invention belongs to the field of image steganalysis, and more specifically relates to an image steganalysis method and system based on a convolutional neural network. Background technique [0002] With the continuous development of Internet technology and image processing technology, digital images have become one of the most widely used information transmission media in daily life. People can upload and download massive digital images on the Internet, but this also gives criminals an advantage. Once criminals use computer technology to steal personal privacy, business secrets and national intelligence through digital images on the network, it will cause very bad effects. Therefore, the security credibility of digital images has become a hot issue in the field of information security. [0003] Digital image steganography technology is an important part of information hiding technology. Unlike information encryption technology, which encrypts orig...

Claims

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Application Information

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IPC IPC(8): G06T1/00G06K9/62
CPCG06T1/0021G06F18/29G06F18/214
Inventor 李璇
Owner SUN YAT SEN UNIV
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