Internship - Controllable Face Anonymization
vor 2 Monaten
Visual privacy preservation is a rising concern in the online world. Face anonymization in images is among such privacy preservation tasks. For the end users, an ideal anonymization algorithm should provide photo-realistic and quantitatively different face generation without breaking the user experience. This translates to ensuring to preserve crucial facial attributes, expressions, and head poses while still providing high-quality and visually-distinct anonymization per end user. Moreover, it is important to give control to the end user for the final anonymization result. In order to facilitate diverse anonymization and enable the end users to have more control over the anonymized face image, various strategies in the generative AI literature could be employed.
Tasks
1. Explore and benchmark the state-of-the-art approaches for controllable face anonymization,
2. Come up with a metric to quantify the anonymization,
**Requirements**:
- Student in computer science, machine learning or relevant field
- Experienced Python coder with good command of PyTorch/TF, NumPy, OpenCV, etc (2+ yrs)
- Familiarity with modern ML concepts of generative models and image manipulation
- Proficient in English
- Independent worker & team player
- Interest to proceed research within a thesis project after internship
**Benefits**:
- 3 months internship with a possible extension into a thesis project
- Experienced supervision and guidance
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