# Approximate Gaussian Mapping for Generative Image Steganography

arXiv: https://arxiv.org/abs/2510.07219 · HTML: https://mlsanity.com/paper/2510.07219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Generating Steganographic Images via Adversarial Training](https://mlsanity.com/paper/1703.00371/) (2017)
- [ImageInWords: Unlocking Hyper-Detailed Image Descriptions](https://mlsanity.com/paper/2405.02793/) (2024)
- [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023)
- [Invisible Steganography via Generative Adversarial Networks](https://mlsanity.com/paper/1807.08571/) (2018)
- [SSGAN: Secure Steganography Based on Generative Adversarial Networks](https://mlsanity.com/paper/1707.01613/) (2017)
- [Generative Steganography Network](https://mlsanity.com/paper/2207.13867/) (2022)
- [Secret-to-Image Reversible Transformation for Generative Steganography](https://mlsanity.com/paper/2203.06598/) (2022)
- [Generative Steganographic Flow](https://mlsanity.com/paper/2305.05838/) (2023)

## Abstract (arXiv)

Ordinary differential equation (ODE)-based diffusion models enable deterministic image synthesis, establishing a reversible mapping suitable for generative steganography. While prevailing methods strictly adhere to a standard normal prior, empirical evidence indicates that controlled deviations from this distribution reduce numerical inversion errors without compromising perceptual quality. Leveraging this observation, the Approximate Gaussian Mapping (AGM) is proposed as a linear transformation strategy that embeds secrets by modulating noise scale and variance. To balance retrieval numerical consistence and security, a two-stage decoupled optimization strategy is introduced to minimize the Kullback-Leibler divergence subject to target bit accuracy constraints. Beyond the proposed method, we conduct a mechanistic analysis of the divergent behaviors between pixel-space and latent-space architectures. The experimental results reveal that the VAE encoder enhances robustness by filtering external perturbations, whereas the structural regularization of the VAE decoder and the semantic variance introduced by text prompts jointly mask embedding artifacts to improve security. Experimental results confirm that pixel-space mplementations maximize embedding capacity for lossless channels, while latent-space approaches offer superior robustness and security suitable for adversarial environments