Due to recent technology advancements, images and videos are used in a variety of application areas, including digital communication, entertainment, social media, journalism, education, biomedical science, and surveillance. Such huge amounts of digital information are transmitted, copied, and disseminated in the form of images and videos in TV channels, newspapers, magazines, over the Internet, and scientific journals. Videos often contain important and authentic information as compared to textual data. However, numerous digital video editing tools have nowadays become freely accessible to the public, making forged videos easy to generate. Malicious video tampering is a type of forgery in which some of the content of the video is replaced with new content in order to convey a different message to what the original video shows. It is therefore crucial to differentiate between original videos and forged ones. Forgery attacks including face spoofing have become more common to use for criminal activities. Consequently, forensic investigations depend more and more on digital data to solve cases. Video forgery detection plays a major part in multimedia forensics to protect the data from malicious use and determine whether or not a video has been tampered with. Digital video forensic analysis helps the court to determine the relevance of evidential information with the greatest precision especially when the outcome of the case depends on a video recordings or the system that produced it. Nowadays, deep learning models have been shown to learn complex patterns in visual data through deep learned features obtained via comvolutional operations. This project will employ filtering-based methods where the smartphone sensor estimation will be conducted by fusing a number of robust filtering techniques to boost performance and then combined with novel deep neural networks to enhance smartphone identification. The aim of this project is also to create deep learning-based algorithms for detecting forged videos and authenticate them. Forged regions will be identified through powerful convolutional neural network based object detection tools such as SSD and YOLO networks. Design and testing of deep learning algorithms for smartphone identification and video forgery detection should entail extracting deep features from video frames, blocks and videos and model connections between the features, smartphone models, and various forgery attacks. The project will also look into existing video datasets as well as traditional and deep learning techniques adopted for video forensics and include them in a comprehensive comparative study.
Academic Enquiries
This project is supervised by Farah Ahmed. For informal queries, please contact [Email Address Removed]. For all other enquiries relating to eligibility or application process please use the email form below to contact Admissions.
Funding Information
Home and International students (inc. EU) are welcome to apply. The studentship is available to Home and International (including EU) students and includes a full stipend at UKRI rates (for 2022/23 full-time study this is £17,668 per year) and full tuition fees. Studentships are also available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £10,600 per year and full tuition fees) in combination with work or personal responsibilities).
Please also see further advice below of additional costs that may apply to international applicants.
Eligibility Requirements:
- Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
- Appropriate IELTS score, if required.
- Applicants cannot apply for this funding if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.
Please note: to be classed as a Home student, candidates must meet the following criteria:
- Be a UK National (meeting residency requirements), or
- have settled status, or
- have pre-settled status (meeting residency requirements), or
- have indefinite leave to remain or enter.
If a candidate does not meet the criteria above, they would be classed as an International student. Applicants will need to be in the UK and fully enrolled before stipend payments can commence, and be aware of the following additional costs that may be incurred, as these are not covered by the studentship.
- Immigration Health Surcharge https://www.gov.uk/healthcare-immigration-application
- If you need to apply for a Student Visa to enter the UK, please refer to the information on https://www.gov.uk/student-visa. It is important that you read this information very carefully as it is your responsibility to ensure that you hold the correct funds required for your visa application otherwise your visa may be refused.
- Check what COVID-19 tests you need to take and the quarantine rules for travel to England https://www.gov.uk/guidance/travel-to-england-from-another-country-during-coronavirus-covid-19
- Costs associated with English Language requirements which may be required for students not having completed a first degree in English, will not be borne by the university. Please see individual adverts for further details of the English Language requirements for the university you are applying to.
How to Apply
For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
For applications to be considered for interview, please include a research proposal of approximately 1,000 words and the advert reference (e.g. RDF23/…).
Deadline for applications: 27 January 2023
Start date of course: 1 October 2023 tbc
Northumbria University is committed to creating an inclusive culture where we take pride in, and value, the diversity of our doctoral students. We encourage and welcome applications from all members of the community. The University holds a bronze Athena Swan award in recognition of our commitment to advancing gender equality, we are a Disability Confident Employer, a member of the Race Equality Charter and are participating in the Stonewall Diversity Champion Programme. We also hold the HR Excellence in Research award for implementing the concordat supporting the career Development of Researchers.
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