# Steganographic Generative Adversarial Networks

## TL;DR

This paper introduces a novel DCGAN-based model for generating image containers that enhance steganography security, effectively deceiving steganalysis tools and improving covert communication methods.

## Contribution

The study presents a new generative model that produces more secure image containers for steganography, outperforming existing methods in evading steganalysis detection.

## Key findings

- Successfully deceives steganalysis classifiers
- Generates more secure image containers
- Enhances steganographic embedding techniques

## Abstract

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.

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Source: https://tomesphere.com/paper/1703.05502