GAN-based carrier image synthesis steganography method

A carrier image and image technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problem of not satisfying the Kirchhowski principle, unable to protect the security of secret information, and the naturalness of carrier images needs to be improved, etc. problems, to achieve the effect of improving naturalness and enhancing safety

Pending Publication Date: 2020-12-22
NINGBO UNIV
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Problems solved by technology

Therefore, although the method proposed in the above literature alleviates the problem of "unable to generate semantic carrier image" to a certain extent, the naturalness of the synthesized carrier image still needs to be improved.
Secondly, there is no key involved in this met

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  • GAN-based carrier image synthesis steganography method
  • GAN-based carrier image synthesis steganography method
  • GAN-based carrier image synthesis steganography method

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Embodiment Construction

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0035] A GAN-based carrier image synthesis steganographic method, comprising the following steps:

[0036] Step 1. Construct a data set, and cut each image in the data set into an image of i*j size, and then form the cut data set into a real image data set; where i and j are both positive integers; for convenience For the subsequent processing of real images, preferably, the values ​​of the size i and j of each real image are equal, of course, they may not be equal, in this embodiment, i=j=64;

[0037] Step 2. Build the generation network G, the discrimination network D, the forensics network F and the extraction network E, and initialize the parameters in the generation network G, the discrimination network D and the extraction network E, and the parameters in the evidence collection network F are preset values; the forensics The preset para...

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Abstract

The invention relates to a GAN-based carrier image synthesis steganography method, which comprises the steps of cutting each image in a data set into images with the same size, and forming a real image data set; constructing a generation network G, a discrimination network D, an evidence obtaining network F and an extraction network E, initializing parameters in the generation network G, the discrimination network D and the extraction network E, and setting the parameters in the evidence obtaining network F as preset values; training the initialized generation network G, discrimination networkD and extraction network E by using a batch training mode to obtain a trained generation network G, discrimination network D and extraction network E; inputting secret information to be embedded anda preset secret key into the trained generation network G to obtain a synthetic carrier image; and inputting the synthesized carrier image and the preset secret key into the trained extraction networkE, and extracting secret information. According to the method, the security of the algorithm is enhanced, and the naturalness of the synthesized carrier image is improved.

Description

technical field [0001] The invention relates to the field of image steganography, in particular to a GAN-based steganographic method for carrier image synthesis. Background technique [0002] Steganography is an important branch of information hiding technology, and its goal is to hide secret information in a digital carrier (such as images, audio and video, etc.) in an imperceptible way, and send it to the receiver. Modern steganographic methods are mainly divided into three basic architectures: carrier modification, carrier selection, and carrier synthesis. Among the above three basic architectures, the development of carrier selection and carrier synthesis steganography lags behind compared to carrier modification steganography. Among them, the carrier synthesis steganography method can only generate non-semantic images such as textures and fingerprints, which leads to very limited use scenarios. [0003] In recent years, among various deep neural networks, GAN has rece...

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Application Information

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IPC IPC(8): G06F21/60G06N3/04G06N3/08
CPCG06F21/602G06N3/08G06N3/045
Inventor 王让定王杰严迪群董理
Owner NINGBO UNIV
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