# Coverless Information Hiding Based on Generative adversarial networks

arXiv: https://arxiv.org/abs/1712.06951 · HTML: https://mlsanity.com/paper/1712.06951/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 10 of 10)

- [Generative Steganography Network](https://mlsanity.com/paper/2207.13867/) (2022)
- [SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI](https://mlsanity.com/paper/2411.11478/) (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)
- [Hiding Images in Deep Probabilistic Models](https://mlsanity.com/paper/2210.02257/) (2022)
- [Plug-and-Hide: Provable and Adjustable Diffusion Generative Steganography](https://mlsanity.com/paper/2409.04878/) (2024)
- [Pixel-Stega: Generative Image Steganography Based on Autoregressive Models](https://mlsanity.com/paper/2112.10945/) (2021)
- [Provably Secure Covert Messaging Using Image-based Diffusion Processes](https://mlsanity.com/paper/2503.10063/) (2025)
- [Training-Free Color-Aware Adversarial Diffusion Sanitization for Diffusion Stegomalware Defense at Security Gateways](https://mlsanity.com/paper/2512.24499/) (2025)
- [Rethinking Security of Diffusion-based Generative Steganography](https://mlsanity.com/paper/2602.10219/) (2026)

## Abstract (arXiv)

Traditional image steganography modifies the content of the image more or less, it is hard to resist the detection of image steganalysis tools. To address this problem, a novel method named generative coverless information hiding method based on generative adversarial networks is proposed in this paper. The main idea of the method is that the class label of generative adversarial networks is replaced with the secret information as a driver to generate hidden image directly, and then extract the secret information from the hidden image through the discriminator. It's the first time that the coverless information hiding is achieved by generative adversarial networks. Compared with the traditional image steganography, this method does not modify the content of the original image. therefore, this method can resist image steganalysis tools effectively. In terms of steganographic capacity, anti-steganalysis, safety and reliability, the experimen shows that this hidden algorithm performs well.