# Secret-to-Image Reversible Transformation for Generative Steganography

arXiv: https://arxiv.org/abs/2203.06598 · HTML: https://mlsanity.com/paper/2203.06598/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2022.

## Built on by (most cited first, 13 of 13)

- [Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection](https://mlsanity.com/paper/2305.18726/) (2023)
- [Joint Lossless Compression and Steganography for Medical Images via Large Language Models](https://mlsanity.com/paper/2508.01782/) (2025)
- [Plug-and-Hide: Provable and Adjustable Diffusion Generative Steganography](https://mlsanity.com/paper/2409.04878/) (2024)
- [Double-Flow-based Steganography without Embedding for Image-to-Image Hiding](https://mlsanity.com/paper/2311.15027/) (2023)
- [DDIM-Driven Coverless Steganography Scheme with Real Key](https://mlsanity.com/paper/2411.06486/) (2024)
- [Provably Secure Covert Messaging Using Image-based Diffusion Processes](https://mlsanity.com/paper/2503.10063/) (2025)
- [CIF: A Constrained Inversion Framework for Reliable Message Extraction in Diffusion-Based Generative Steganography](https://mlsanity.com/paper/2508.00434/) (2025)
- [SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication](https://mlsanity.com/paper/2509.04803/) (2025)
- [Approximate Gaussian Mapping for Generative Image Steganography](https://mlsanity.com/paper/2510.07219/) (2025)
- [Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways](https://mlsanity.com/paper/2512.24499/) (2025)
- [DTAMS: High-Capacity Generative Steganography via Dynamic Multi-Timestep Selection and Adaptive Deviation Mapping in Latent Diffusion](https://mlsanity.com/paper/2602.01160/) (2026)
- [Rethinking Security of Diffusion-based Generative Steganography](https://mlsanity.com/paper/2602.10219/) (2026)
- [Histogram-constrained Image Generation](https://mlsanity.com/paper/2606.31683/) (2026)

## Abstract (arXiv)

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent space with multivariate Gaussian distribution and image space with a complex distribution. In the process of S2I transformation, guided by a given secret message, we construct a latent vector and then map it to a generated image by the Glow model, so that the secret message is finally transformed to the generated image. Owing to good efficiency and reversibility of S2IRT scheme, the proposed steganographic approach achieves both high hiding capacity and accurate extraction of secret message from generated image. Furthermore, a separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the robustness to common image attacks. The experiments demonstrate the proposed steganographic approaches can achieve high hiding capacity (up to 4 bpp) and accurate information extraction (almost 100% accuracy rate) simultaneously, while maintaining desirable anti-detectability and imperceptibility.