Hidden Space Vector Coding-Based Generative Image Steganography With Diffusion Model

Recently, generative image steganography that hides messages in generated images has been a promising technique to achieve effective secret communication, due to its strong anti-steganalysis ability. However, existing generative image steganography still performs poorly in the image generation quality and hiding capacity. To resolve these shortcomings, this work proposes a Diffusion-based Generative Image Steganography (DGIS) approach, which includes a Diffusion Model (DM)-based stego-image generator, an INN-based secret autoencoder, and a UNet-based secret extractor. In this approach, the secret message is mapped to a hidden space vector to control the process of stego-image generation. Experimental results demonstrate that the proposed DGIS significantly improves image generation quality and message hiding capability.

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Publication Details

Journal
IEEE Transactions on Consumer Electronics
Published
2025-09-30
DOI
https://doi.org/10.1109/tce.2025.3616171
Citations
1
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
1.28
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article

Hidden Space Vector Coding-Based Generative Image Steganography With Diffusion Model

Huilin Ge, Weisong Liu, Yuhuan Liu, Zhili Zhou et al.
1 citations
IEEE Transactions on Consumer Electronics
Advanced Steganography and Watermarking Techniques
1.28
article

Hidden Space Vector Coding-Based Generative Image Steganography With Diffusion Model

Huilin Ge, Weisong Liu, Yuhuan Liu, Zhili Zhou, Haijun Zhang, Yujiang Li, Peng Liu, Zichuan Li
article en
1 citations

Abstract

Recently, generative image steganography that hides messages in generated images has been a promising technique to achieve effective secret communication, due to its strong anti-steganalysis ability. However, existing generative image steganography still performs poorly in the image generation quality and hiding capacity. To resolve these shortcomings, this work proposes a Diffusion-based Generative Image Steganography (DGIS) approach, which includes a Diffusion Model (DM)-based stego-image generator, an INN-based secret autoencoder, and a UNet-based secret extractor. In this approach, the secret message is mapped to a hidden space vector to control the process of stego-image generation. Experimental results demonstrate that the proposed DGIS significantly improves image generation quality and message hiding capability.

IEEE Transactions on Consumer ElectronicsVol. 71(4)
Nanjing University of Information Science and Technology (CN), Harbin Institute of Technology (CN), University Town of Shenzhen (CN), Guangzhou University (CN), Jiangsu University of Science and Technology (CN), Shanghai Public Security Bureau (CN), Criminal Investigation Police University of China (CN)
Openalex Percentile: Top 15%
Advanced Steganography and Watermarking Techniques
1.28
DOISourceOpenAlex
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Hidden Space Vector Coding-Based Generative Image Steganography With Diffusion Model — Huilin Ge, Weisong Liu, et al. · IEEE Transactions on Consumer Electronics (2025) | TGRS Research Map | TGRS