Steganography GANs

Abstract

1. Introduction

Image steganography usings Generative Adverserial Networks

Steganography is a generic term that denotes all those techniques that somehow try to hide information within other forms of data. Differently from cryptography, which aims to hide messages by manipulation of the data, steganography aims to hide the existence of the information itself.

In this paper we are going to present steganography applied to images and audio, and in particular we will analyze the benefits that generative adversarial training produces in this context. The method used consists in three networks which works toghether: the first, which from now on we will refer to as \emph{encoder}, responsible for hiding the information, the second, named \emph{decoder}, responsible for recovering the secret message and the third, called the \emph{critic} which detect the presence of the hided information. The real place in which the adversarial training takes place is between the encoder and the critic, this last one provides feedback on the performance of the second and ensure the encoder to produce realistic images as much as possible.

2. Objective

Image steganography usings Generative Adverserial Networks

This project demonstrates how Jupyter Notebook can be applied to a real-world Cybersecurity & Ethical Hacking problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/garg-akash/Steganography_GANs.git
cd Steganography_GANs

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add logging and alert notifications (email / Telegram)
• Write a threat model document for the tool
• Package it with Docker for safe lab testing

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which threat or attack does this project defend against?
2. What detection or protection technique is used and what are its limits?
3. How are false positives and false negatives handled?
4. Which cryptographic algorithms or security standards are involved?
5. What legal and ethical rules apply when testing a tool like this?

9. Source Code & License

This project is developed by garg-akash and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.