In this module, you will learn about the scientific techniques used to collect probative facts from digital data often in relation to cyberphysical crime.
The module will focus on a subfield of digital forensics that involves analysing image and video data for forensic purposes. This subfield (digital image forensics) is getting increasingly important since digital cameras and sophisticated photo editing softwares have become commonplace. Advanced machine learning methods are now capable of generating fake images and videos that can easily fool humans. Image forensic experts develop and use computational techniques to identify photo forgery, detect image sources and collect crime-related evidences from image data.
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:
The 'Sensor based forensics' section in the syllabus is 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 paper.
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.
Students can register for this module without taking any assessment.
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Individual practical assignment 1 | 15% | Yes (extension) | |
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Individual practical assignment. |
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| Individual practical assignment 2 | 15% | Yes (extension) | |
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Individual practical assignment. |
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| In-person Examination | 70% | No | |
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Exam
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| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
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resit examination
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Written feedback on coursework will be provided to the students.
Students must have studied the content of CS131 Mathematics for Computer Scientists II or CS137 Discrete Mathematics II or ES193 Engineering Mathematics or have studied equivalent material.
This module is Optional for:
This module is Option list A for: