# Generative Steganography Network

arXiv: https://arxiv.org/abs/2207.13867 · HTML: https://mlsanity.com/paper/2207.13867/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [SteganoGAN: High Capacity Image Steganography with GANs](https://mlsanity.com/paper/1901.03892/) (2019)
- [CycleGAN, a Master of Steganography](https://mlsanity.com/paper/1712.02950/) (2017)
- [Generating Steganographic Images via Adversarial Training](https://mlsanity.com/paper/1703.00371/) (2017)
- [Coverless Information Hiding Based on Generative adversarial networks](https://mlsanity.com/paper/1712.06951/) (2017)
- [Generative Steganography by Sampling](https://mlsanity.com/paper/1804.10531/) (2018)
- [Coverless information hiding based on Generative Model](https://mlsanity.com/paper/1802.03528/) (2018)

## Built on by (most cited first, 15 of 15)

- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Generative Steganography Diffusion](https://mlsanity.com/paper/2305.03472/) (2023)
- [Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection](https://mlsanity.com/paper/2305.18726/) (2023)
- [From Covert Hiding to Visual Editing: Robust Generative Video Steganography](https://mlsanity.com/paper/2401.00652/) (2024)
- [Plug-and-Hide: Provable and Adjustable Diffusion Generative Steganography](https://mlsanity.com/paper/2409.04878/) (2024)
- [Securing Fixed Neural Network Steganography](https://mlsanity.com/paper/2309.09700/) (2023)
- [Effective Message Hiding with Order-Preserving Mechanisms](https://mlsanity.com/paper/2402.19160/) (2024)
- [LLMs can hide text in other text of the same length](https://mlsanity.com/paper/2510.20075/) (2025)
- [Unified High-binding Watermark for Unconditional Image Generation Models](https://mlsanity.com/paper/2310.09479/) (2023)
- [Universal Perturbation-based Secret Key-Controlled Data Hiding](https://mlsanity.com/paper/2311.01696/) (2023)
- [Provably Secure Covert Messaging Using Image-based Diffusion Processes](https://mlsanity.com/paper/2503.10063/) (2025)
- [Approximate Gaussian Mapping for Generative Image Steganography](https://mlsanity.com/paper/2510.07219/) (2025)
- [Rethinking Security of Diffusion-based Generative Steganography](https://mlsanity.com/paper/2602.10219/) (2026)
- [PRoADS: Provably Secure and Robust Audio Diffusion Steganography with latent optimization and backward Euler Inversion](https://mlsanity.com/paper/2603.10314/) (2026)
- [No Prompt, No Leaks: A Robust Generative Steganography Framework via Prompt-Free Diffusion](https://mlsanity.com/paper/2606.31427/) (2026)

## Abstract (arXiv)

Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized for their poor performances. In this paper, we propose an advanced generative steganography network (GSN) that can generate realistic stego images without using cover images. We firstly introduce the mutual information mechanism in GS, which helps to achieve high secret extraction accuracy. Our model contains four sub-networks, i.e., an image generator (G), a discriminator (D), a steganalyzer (S), and a data extractor (E). D and S act as two adversarial discriminators to ensure the visual quality and security of generated stego images. E is to extract the hidden secret from generated stego images. The generator G is flexibly constructed to synthesize either cover or stego images with different inputs. It facilitates covert communication by concealing the function of generating stego images in a normal generator. A module named secret block is designed to hide secret data in the feature maps during image generation, with which high hiding capacity and image fidelity are achieved. In addition, a novel hierarchical gradient decay (HGD) skill is developed to resist steganalysis detection. Experiments demonstrate the superiority of our work over existing methods.