M2 Internship: Zero-shot Deepfake Detection

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:Convention de stage

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Stagiaire de la recherche

**A propos du centre ou de la direction fonctionnelle**:
The Inria center at the University of Rennes is one of eight Inria centers and has more than thirty research teams. The Inria center is a major and recognized player in the field of digital sciences. It is at the heart of a rich ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes.

**Contexte et atouts du poste**:
While there is an increasing number of approaches for detecting whether an image or video has been the result of AI-based synthesis or manipulation, a key issue that detectors face is the generalization to unseen generative architectures. A significant hurdle in the development of robust deepfake detection systems is the limited availability of diverse datasets for training and evaluation purposes.

A possible solution to gain generalization and robustness is to shift to a different paradigm, where models are only trained on real videos, with the goal to detect manipulated videos based on their anomalous behavior [1]. Recent work has focused on verifying audio-visual consistency with different strategies [2-5].

On the other hand, few studies explore an approach that modelize not just real videos, but more precisely the real videos of a particular individual [6-9]. However those approaches have not been evaluated on same datasets, and do not provide sufficient details on performance for different types of manipulation (ID-replaced or ID-remained), robustness to degradation like compression or blurring, and false positives, that are a key issue of synthetic media detection methods because they compromise the trust of journalists and fact-checkers on their results.

**Mission confiée**:
The idea is to combine these 2 approaches, i.e. the possibility of modeling a person, and proposing a solution to detect manipulations independent of the modification technique. The aim of the internship is to answer the following questions:

- To what extent are ID-Remained cases are more difficult than ID-Replaced ones?
- Are the behavioral characteristics proposed in the literature really relevant, discriminative and robust?
- How robust are these methods to low quality and high compression?
- What is the minimum number of videos needed to learn the model of an individual?

**Principales activités**:

- Identify in the literature the most promising features and zero-shot methods
- Set up a robust evaluation framework to assess the various aspects of performance
- Evaluate the performances of selected methods, either existing ones or proposed ones
- Results analysis to pinpoint difficulties of both the dataset and the methods

**Compétences**:

- Master in Computer Sciences, with proficiency in python and its libraries for deep learning
- General background in computer vision and machine learning
- Understanding of deep learning methodologies and techniques;
- Proficiency in data handling, particularly in video processing

**Avantages**:

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

**Rémunération**:
Brut mensuel de 1766,92 euros.

**Informations générales**:

- **Thème/Domaine**: Sécurité et confidentialité
- **Ville**: Rennes
- **Centre Inria**: Centre Inria de l'Université de Rennes
- **Date de prise de fonction souhaitée**: 2024-03-01
- **Durée de contrat**: 4 mois
- **Date limite pour postuler**: 2024-11-17

**Consignes pour postuler**:
**Sécurité défense**:
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

**Politique de recrutement**:
Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

**Contacts**:

- **Équipe Inria**: ARTISHAU
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