# SSGAN: Secure Steganography Based on Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1707.01613 · HTML: https://mlsanity.com/paper/1707.01613/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2017.

## Built on by (most cited first, 18 of 18)

- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography](https://mlsanity.com/paper/2305.16936/) (2023)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [Multi-Stage Residual Hiding for Image-into-Audio Steganography](https://mlsanity.com/paper/2101.01872/) (2021)
- [PRIS: Practical robust invertible network for image steganography](https://mlsanity.com/paper/2309.13620/) (2023)
- [Robust Message Embedding via Attention Flow-Based Steganography](https://mlsanity.com/paper/2405.16414/) (2024)
- [Diffusion-Based Hierarchical Image Steganography](https://mlsanity.com/paper/2405.11523/) (2024)
- [Multitask Identity-Aware Image Steganography via Minimax Optimization](https://mlsanity.com/paper/2107.05819/) (2021)
- [VisGuard: Securing Visualization Dissemination through Tamper-Resistant Data Retrieval](https://mlsanity.com/paper/2507.14459/) (2025)
- [Hiding Data in Colors: Secure and Lossless Deep Image Steganography via Conditional Invertible Neural Networks](https://mlsanity.com/paper/2201.07444/) (2022)
- [Exploiting Fine-Grained DCT Representations for Hiding Image-Level Messages within JPEG Images](https://mlsanity.com/paper/2305.06582/) (2023)
- [Implicit Steganography Beyond the Constraints of Modality](https://mlsanity.com/paper/2312.05496/) (2023)
- [STCL:Curriculum learning Strategies for deep learning image steganography models](https://mlsanity.com/paper/2504.17609/) (2025)
- [LatentGAN Autoencoder: Learning Disentangled Latent Distribution](https://mlsanity.com/paper/2204.02010/) (2022)
- [Deniable Steganography](https://mlsanity.com/paper/2205.12587/) (2022)
- [DKiS: Decay weight invertible image steganography with private key](https://mlsanity.com/paper/2311.18243/) (2023)
- [Neural Cover Selection for Image Steganography](https://mlsanity.com/paper/2410.18216/) (2024)
- [Approximate Gaussian Mapping for Generative Image Steganography](https://mlsanity.com/paper/2510.07219/) (2025)

## Abstract (arXiv)

In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method.