Universal steganography method based on deep learning

Abstract

The invention discloses a general steganography method based on deep learning. The method comprises the following steps: S1, carrying out hiding processing on a sender; dividing secret information tobe hidden into n groups of information fragments, wherein each group of information fragments correspond to one category label, a deep learning model is adopted, the category label and random noise are used as drive, a pseudo-natural image of a specified category is generated, and the pseudo-natural image is used as a secret-containing image input channel after hiding processing; and S2, carryingout extraction processing at a receiver: inputting the secret-containing image into a discriminator by the receiver to carry out image authenticity identification and image category judgment, then sending the image category information into a function converter to be processed to obtain a secret information fragment, and decoding the secret information fragment to obtain original secret information. According to the invention, the security and confidentiality of information transmission can be greatly improved.

Description

technical field [0001] The invention belongs to the technical field of information hiding, and in particular relates to a general steganography method based on deep learning. Background technique [0002] As an important way of information security transmission, information hiding is of great significance to national security and information security. The concept of information hiding includes many aspects, mainly including covert channel, steganography, anonymous communication and copyright representation. Most of the early information hiding methods can guarantee the visual quality of carrier images in BMP, JPEG, GIF and other formats, but they do not consider much about the statistical characteristics of carrier data. Subsequently, scholars proposed some information hiding methods that can maintain certain statistical characteristics without abnormalities, but the security is still not satisfactory. For example: the LSB matching method avoids statistical asymmetry and h...

AI Technical Summary

Problems solved by technology

This technology judges the existence of secret information based on the statistical anomalies of carrier data caused by information embedding, which has posed a serious threat to the effect and security of information hiding; (2), the current mainstream information hiding methods are empirically set when modifying carrier data The loss index, and then using the idea of ​​"encoding method to minimize the total loss", there are few breakthrough results; (3), compared with the wide application of information hiding, the theoretical research of information hiding seems to be lagging behind, and can not provide information for the application and development of technology. strong support

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