# Recent Advances of Image Steganography with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1907.01886 · HTML: https://mlsanity.com/paper/1907.01886/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Secret Collusion among AI Agents: Multi-Agent Deception via Steganography](https://mlsanity.com/paper/2402.07510/) (2024)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space](https://mlsanity.com/paper/2201.06415/) (2022)
- [Boosting Digital Safeguards: Blending Cryptography and Steganography](https://mlsanity.com/paper/2404.05985/) (2024)
- [Provably Secure Covert Messaging Using Image-based Diffusion Processes](https://mlsanity.com/paper/2503.10063/) (2025)

## Abstract (arXiv)

In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.