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Automatic DeepFakes Generation and Identification

  • Full or part time
  • Application Deadline
    Friday, February 14, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Application details:
Reference number: HC/CO/2020
Start date of studentship: 1 October 2020
Closing date of advert: 14 February 2020

Supervisors:
Primary supervisor: Haibin Cai
Secondary supervisor:
Loughborough University is a top-ten rated university in England for research intensity (REF2014). In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College to help you succeed in your research and future career.
Loughborough University has a flexible working and maternity/parental leave policy (https://www.lboro.ac.uk/services/hr/leave-absence/family-leave/) and is a Stonewall Diversity Champion providing a supportive and inclusive environment for the LGBT+ community. The University is also a member of the Race Equality Charter which aims to improve the representation, progression and success of minority ethnic staff and students. The School of Science is a recipient of the Athena SWAN bronze award for gender equality.
Find out more: http://www.lboro.ac.uk/study/postgraduate/supporting-you/research/
Full Project Detail:

The fake videos generated by Artificial Intelligence (AI) technologies, known as DeepFakes, have come to a stage where it is hard for a human to distinguish them. They can be generated easily using recently developed tools like FaceSwap, Face2Face, and DeepFaceLab. The subject in the fake videos can appear to do some activities that do not happen, thus might cause serious consequences. The spread of these manipulated videos over the internet is a huge danger in damaging businesses, crippling reputations, misleading elections, etc. Thus, it is important to develop advanced deep learning technologies to fight against synthetically generated fake information.
The purpose of this project is to prevent the spread of these misleading fake videos by developing effective Deepfake detection algorithms. This will involve the automatic generation of high-quality synthetic videos and the identification of these videos. Different deep learning models like Convolutional Neural Networks, Generative Adversarial Networks, and Recurrent Neural Network will be explored to improve the generation and identification performance.
The successful candidate will have the unique opportunity to work collaboratively with the Loughborough AI research team in Computer Science for three years. The research will also provide opportunities to attend academic conferences, summer schools, and other training courses to improve technical skills. You will master advanced deep learning techniques and have excellent career prospects on the successful completion of the PhD.
Find out more: https://www.lboro.ac.uk/science/study/postgraduate-research/studentships/

Entry requirements:
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Computer Science or a related subject. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: Computer Vision or Deep Learning.

Funding information:
This studentship will be awarded on a competitive basis to applicants who have applied to this project and/or any of the advertised projects prioritised for funding by the School of Science.
The 3-year studentship provides a tax-free stipend of £15,009 (2019 rate) per annum (in line with the standard research council rates) for the duration of the studentship, plus tuition fees at the UK/EU rate. This studentship is only available to those who are eligible to pay UK/EU fees.
Contact details:
Name: Haibin Cai
Email address:
Telephone number: +44(0)1509 223157

How to apply:

All applications should be made online at http://www.lboro.ac.uk/study/apply/research/. Under programme name, select Computer Science.

Please quote reference number: HC/CO/2020.

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