Image Steganography and Style Transformation Based on Generative Adversarial Network

Li Li, Xinpeng Zhang (), Kejiang Chen, Guorui Feng, Deyang Wu and Weiming Zhang
Additional contact information
Li Li: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Xinpeng Zhang: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Kejiang Chen: CAS Key Laboratory of Electro-Magnetic Space Information, University of Science and Technology of China, Hefei 230027, China
Guorui Feng: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Deyang Wu: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
Weiming Zhang: CAS Key Laboratory of Electro-Magnetic Space Information, University of Science and Technology of China, Hefei 230027, China

Mathematics, 2024, vol. 12, issue 4, 1-11

Abstract: Traditional image steganography conceals secret messages in unprocessed natural images by modifying the pixel value, causing the obtained stego to be different from the original image in terms of the statistical distribution; thereby, it can be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial-Intelligence-Generated Content (AIGC) becoming popular on social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image-style-transformation neural network with a steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder–decoder model, and finally, generates a styled image containing the secret messages at the same time. An adversarial training technique was applied to enhance the imperceptibility of the generated art-style stego image from plain-style-transferred images. The lack of the original cover image makes it difficult for the opponent learning steganalyzer to identify the stego. The proposed approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of three bits per pixel for a color image, according to the experimental results.

Keywords: Generative Adversarial Network (GAN); image steganography; style transfer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/4/615/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/4/615/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:4:p:615-:d:1341551

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().