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ADAPTIVE STEGANOGRAPHY USING DEEP LEARNING TECHNIQUES

OVERVIEW

This projects implements deep steganography techniques and integrates them with existing steganography methods. The project is designed to hide messages within images and extract them to ensure visual similarity and security between original and embedded images. The project uses Convulational Neural Networks and uses three existing image steganography methods which are Least Significant Bit (LSB), Discrete Cosine transform (DCT) and Spatial Universal Wavelet Relative distortion (S-UNIWARD). The projects compares between the original methods and the methods integrated with deep learning to answer the research question while also meeting the objectives stated in the Dissertation Document.

image

AUTHORS

Rutendo Beverly Musara

Student ID: 9733633

INSTALLATIONS

The main enviroment used for this project was Jupyter Notebook

ACKNOWGEMENTS

I would like to acknowledge Coventry University as this Dissertation is submitted on the behalf of this Univeristy

Dr Norlaily Yaacob - Project Supervisor who helped me with this project

Liang Wang - Developed this dataset used for this project

Figshare - Obtained dataset from this database platform

LICENSES

The dataset used for this Disseration was licensed by CC BY 4.0 DEEDS

Copyright (c) Project Jupyter Contributors. Distributed under the terms of the 3-Clause BSD License.

This project is licensed under the MIT license

LICENCES FOR THE STEGANOGRAPHY TOOLS USED IN THE PROJECT Aperisolve And

FotoForensics

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This is my final dissertation project on deep steganography

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