Idiap has a new opening for an Internship on Deepfake and morphing attack detection in face recognition

The Idiap Research Institute invites applications for one internship position in biometrics security and privacy. The duration of the internship is 6-month.

Recent advances in Deep Learning allow for the creation of high quality tampered video content: Deepfakes. A Deep Neural Network can be used to replace a face by another face and to perform a presentation attack (impersonation).
Accessible open source software and apps for such face swapping lead to large amounts of synthetically generated Deepfake videos appearing in social media and news, posing a significant technical challenge for detection and filtering of such content.
Traditional face morphing has been shown to be challenging for face recognition systems and several detection methods has been proposed.
However, the high quality of DeepFakes calls for automated ways to detect these synthetic faces.

The internship will investigate:
* traditional methods to morph faces,
* state-of-the-art methods to generate DeepFakes,
* innovative methods to detect morph faces and DeepFakes,
* and if time allows new methods to morph two faces into a single one using generative adversarial networks (GANs).

[ ]
[ ]

The proposed research will be carried out in the Biometric Security and Privacy group (Dr. Sebastien Marcel, ) at Idiap. The research will rely on previous knowledge and softwares developed at Idiap, more specifically Bob toolkit ( ).
Reproducible research is a cornerstone of the project. Hence a strong involvement in open source libraries such as Bob are expected.

The ideal candidate should hold a Bachelor degree or a Master degree (or in its final year) in computer science, electrical engineering or related fields. She or he should have a background in statistics, applied mathematics, optimization, linear algebra and signal processing. The applicant should also have strong programming skills and be familiar with Python, C/C++ (MATLAB is not a plus), various scripting languages and with the Linux environment. Knowledge in machine learning and more particularly deep learning (TensorFlow and pyTorch) is an asset. Shortlisted candidate may undergo a series of tests including technical reading and writing in English and programming (in Python and/or C/C++).

Prospective candidates should apply through the Idiap Online Recruitment System (ORS) only. Inquiries about the position can be addressed to Dr. Sébastien Marcel.
The project start date is expected to be early 2020. The position will remain open until filled.