High-capacity image steganography and recovery method based on reversible neural network

A neural network and restoration method technology, applied in the field of large-capacity image steganography and restoration based on reversible neural networks, can solve the problems of not being able to obtain high-quality confidential images and restored images at the same time, and the capacity of image steganography methods is small, and achieve Steganography capacity enhancement, good effect effect

Active Publication Date: 2021-06-01
NANKAI UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that the existing image steganography method has a small capacity and cannot obtain high-quality c

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  • High-capacity image steganography and recovery method based on reversible neural network
  • High-capacity image steganography and recovery method based on reversible neural network
  • High-capacity image steganography and recovery method based on reversible neural network

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Embodiment Construction

[0026] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0027] refer to figure 1 , represents the flow chart of image steganography and restoration. The general steps of image steganography and restoration shown in the figure are as follows: first, determine the carrier image and hidden image, and embed the hidden image into the carrier image through the process of "image steganography" to obtain the The secret-carrying images with similar images, and then the secret-carrying images with hidden information go through the process of "hidden image recovery" to reconstruct all the hidden images.

[0028] refer to figure 2 , which represents the model structure of the reversible neural network. The large-capacity image steganography and restora...

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Abstract

The invention discloses a high-capacity image steganography and recovery method based on a reversible neural network. The purpose of the method is to embed one or more hidden images into a single carrier image and recover all hidden images from a secret-carrying image. According to the method, an image steganography model capable of realizing bidirectional mapping is designed. The model is formed by cascading reversible modules comprising a carrier branch and a hidden branch, forward mapping embeds a hidden image into a carrier image to synthesize a secret-carrying image, and reverse mapping separates and recovers the carrier image and the hidden image from a single secret-carrying image. According to the method, the reversibility of the model is fully utilized, all parameters are shared in the forward steganography process and the reverse recovery process, the high-quality secret-carrying image and the high-quality recovered image can be obtained at the same time, and the steganography capacity is effectively improved.

Description

technical field [0001] The invention belongs to the field of information hiding, and in particular relates to a large-capacity image steganography and recovery method based on a reversible neural network. Background technique [0002] Steganography is a technique of hiding secret information by embedding it into a carrier. Unlike cryptography, which hides the meaning of a message (even if it is difficult to understand), steganography aims to hide the existence of a secret message. Therefore, image steganography refers to the process of hiding information in image files. The image used to store hidden data is called a cover image, and the image containing hidden information generated by steganography is called a secret image. Today, image steganography has been applied in many practical fields such as digital communication, copyright protection, information authentication, and e-commerce. [0003] A well-designed image steganography system needs to have the characteristics...

Claims

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Application Information

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IPC IPC(8): G06T1/00G06N3/04
CPCG06T1/0021G06N3/04G06T2201/0203
Inventor 卢少平王榕钟涛
Owner NANKAI UNIV
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