View Proposal

---

Proposer

Idris Ibrahim

Title

Image Steganography Creation and Detection Using Machine Learning

Goal

This project aims to explore image steganography as a covert security threat by embedding hidden metadata within digital images without affecting their visual appearance. It seeks to develop a machine learning–based steganalysis model capable of detecting images that contain hidden information. The project also aims to evaluate the effectiveness of machine learning techniques in identifying steganographic images and to assess the security implications of steganography in real-world cyber attack scenario

Description

This project investigates the use of image steganography as a covert communication technique in computer security. It involves developing a system to embed malicious or sensitive metadata within digital images using steganographic methods, such that the images appear visually unchanged. In parallel, the project designs and trains a machine learning–based steganalysis model capable of detecting whether an image contains hidden data. The work highlights both the security risks posed by steganography and the effectiveness of machine learning as a defensive detection approach.

Resources

Background

Url

Difficulty Level

Moderate

Ethical Approval

InterfaceOnly

Number Of Students

1

Supervisor

Idris Ibrahim

Keywords

Degrees

Bachelor of Science in Computer Systems
Master of Science in Computer Science for Cyber Security
Master of Science in Computing (2 Years)
Bachelor of Science in Computing Science
Bachelor of Science in Computer Science (Cyber Security)
Postgraduate Diploma in Artificial Intelligence
MSc Applied Cyber Security