# Steganographic Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1703.05502 · HTML: https://mlsanity.com/paper/1703.05502/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 21 of 21)

- [RoSteALS: Robust Steganography using Autoencoder Latent Space](https://mlsanity.com/paper/2304.03400/) (2023)
- [Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography](https://mlsanity.com/paper/2107.09287/) (2021)
- [EvilModel: Hiding Malware Inside of Neural Network Models](https://mlsanity.com/paper/2107.08590/) (2021)
- [EvilModel 2.0: Bringing Neural Network Models into Malware Attacks](https://mlsanity.com/paper/2109.04344/) (2021)
- [Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography](https://mlsanity.com/paper/2512.20168/) (2025)
- [Multi-Stage Residual Hiding for Image-into-Audio Steganography](https://mlsanity.com/paper/2101.01872/) (2021)
- [Information hiding cameras: optical concealment of object information into ordinary images](https://mlsanity.com/paper/2401.07856/) (2024)
- [From Covert Hiding to Visual Editing: Robust Generative Video Steganography](https://mlsanity.com/paper/2401.00652/) (2024)
- [Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness](https://mlsanity.com/paper/2303.13713/) (2023)
- [Multitask Identity-Aware Image Steganography via Minimax Optimization](https://mlsanity.com/paper/2107.05819/) (2021)
- [Deep Learning for Predictive Analytics in Reversible Steganography](https://mlsanity.com/paper/2106.06924/) (2021)
- [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)
- [A Novel Approach to Image Steganography Using Generative Adversarial Networks](https://mlsanity.com/paper/2412.00094/) (2024)
- [StegOT: Trade-offs in Steganography via Optimal Transport](https://mlsanity.com/paper/2509.11178/) (2025)
- [On the predictability in reversible steganography](https://mlsanity.com/paper/2202.02518/) (2022)
- [Deniable Steganography](https://mlsanity.com/paper/2205.12587/) (2022)
- [Image Generation Network for Covert Transmission in Online Social Network](https://mlsanity.com/paper/2207.10292/) (2022)
- [Boosting Digital Safeguards: Blending Cryptography and Steganography](https://mlsanity.com/paper/2404.05985/) (2024)
- [Steganographic Embeddings as an Effective Data Augmentation](https://mlsanity.com/paper/2502.15245/) (2025)
- [Quantum Gatekeeper: Multi-Factor Context-Bound Image Steganography with VQC Based Key Derivation on Quantum Hardware](https://mlsanity.com/paper/2604.26413/) (2026)

## Abstract (arXiv)

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.