In this module, you will learn about the scientific techniques used to collect, preserve, and analyse digital evidence, often in the context of cybercrime and cyber-physical incidents.
The module focuses on a subfield of digital forensics concerned with the forensic analysis of image and video data. Digital image forensics has become increasingly important as digital cameras, sophisticated editing software, and AI-based image generation tools have become widely accessible. Modern machine learning methods are now capable of generating highly realistic fake images and videos that can be difficult for humans to detect. This module explores the computational techniques used to identify image manipulation, determine image provenance, and extract evidential information from digital image data for forensic and investigative purposes.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
The module will deal with core concepts and enabling methodologies in multimedia-based digital forensics. It will also examine current applications, and address theoretical and practical challenges. More specifically the syllabus will cover:
By the end of the module, students should be able to:
Reading lists can be found in Talis
The 'source camera identification', and 'deepfake detection' sections in the syllabus are based on recent research advances on this topic. The students will be reading from research papers instead of textbooks. They will also implement the techniques described in the research papers.
Knowledge of types of image forgery
State-of-the-art forensics methods
Forensics algorithms
Forensics practices.
Programming
Knowledge of image and video processing
Knowledge of basic probability, linear algebra and transforms
Report writing
Analytical thinking.
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Practical classes | 9 sessions of 1 hour (6%) |
| Private study | 121 hours (81%) |
| Total | 150 hours |
Studying textbook, lecture notes, other resources provided
Solving the exercise questions and practice problems, given during the lectures
Coursework preparation including programming and report preparation.
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Individual practical assignment. | 30% | No | |
| In-person Examination | 70% | No | |
|
Exam
|
|||
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
|
resit examination
|
|||
Written feedback on coursework will be provided to the students.
This module is Optional for:
This module is Core option list A for:
This module is Core option list B for: