Supervisor

Suitable for

Abstract

Steganography means hiding a hidden payload within an apparently-innocent cover, usually an item of digital media (in this project: images). Steganalysis is the art of detecting that hiding took place. The most effective ways to detect steganography are machine learning algorithms applied to "features" extracted from the images, trained on massive sets of known cover and stego objects. The images are thus turned into points in high-dimensional space. We have little intuition as to the geometrical structure of the features (do images form a homogeneous cluster? do they scale naturally with image size?), or how they are altered under embedding (do they move in broadly the same direction? is there is linear separator of cover from stego?). This project involves the implementation of visualization tools for features, extracting them from images and then projecting them onto 2-dimensional space in interesting ways, while illustrating the effects of embedding. Visual style and clarity will be important.

Prerequisites:

Computer graphics and linear algebra

Undergraduate students who wish to enquire about a project for 2017-18 are welcome to contact Prof Ker but should note that the response may be delayed as he is on sabbatical.