(M/F) thesis offer : Protecting 3D models with robust watermarking techniques

European Commission; France; Sur place; EUR 16 000 - 27 000; Plein temps

Résumé du poste

A leading research institution in France is seeking a PhD student to work on innovative methods for watermarking 3D models using deep learning. This role focuses on developing reliable algorithms to enhance copyright protection of 3D models, which is crucial in today's digital landscape. Qualified candidates should have a Master's degree and experience in 3D model processing.

Qualifications

• Master's degree in a relevant field required.
• Experience with 3D models and machine learning preferred.
• Proficiency in Python programming.

Responsabilités

• Develop robust watermarking methods for 3D models using deep learning.
• Conduct literature reviews and implement a neural network for watermarking.
• Study interactions between different types of mesh information.

Connaissances

3D model processing

Machine learning

Python programming

Excellent communication

Formation

Master/Engineer degree in Computer Science or related fields

Description du poste

Organisation/Company CNRS Department Grenoble Images Parole Signal Automatique Research Field Engineering Computer science Mathematics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 16 Jul 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The Gipsa-lab is a joint research laboratory of the CNRS, Grenoble-INP -UGA and the University of Grenoble Alpes. It is under agreement with Inria and the Observatory of Sciences of the Universe of Grenoble. He conducts theoretical and applied research on AUTOMATICS, SIGNAL, IMAGES, SPEECH, COGNITION, ROBOTICS and LEARNING.
Multidisciplinary and at the interface between the human, the physical and digital worlds, our research is confronted with measurements, data, observations from physical, physiological and cognitive systems. They focus on the design of methodologies and algorithms for processing and extracting information, decisions, actions and communications that are viable, efficient and compatible with physical and human reality. Our work is based on mathematical and computer theories for the development of models and algorithms, validated by hardware and software implementations.
By relying on its platforms and partnerships, Gipsa-lab maintains a constant link with applications in a wide variety of fields: health, environment, energy, geophysics, embedded systems, mechatronics, processes and industrial systems, telecommunications, networks, transport and vehicles, operational safety and security, human-computer interaction, linguistic engineering, physiology and biomechanics, etc.
Due to the nature of its research, Gipsa-lab is in direct and constant contact with the economic environment and society.
Its potential as teacher-researchers and researchers is invested in training at the level of universities and engineering schools on the Grenoble site (Grenoble Alpes University).
Gipsa-lab develops its research through 16 teams or themes organized into 4 divisions:
• Automatic and Diagnosis (PAD)
• Data Science (PSD)
• Speech and Cognition (PPC)
• Geometries, Learning, Information and Algorithms (GAIA).
The staff supporting research (38 engineers and technicians) is distributed in the common services distributed within 2 divisions:
• The Administrative and Financial Pole
• The Technical Pole
Gipsa-lab has around 150 permanent staff, including 70 teacher-researchers and 41 researchers. It also welcomes guest researchers and post-docs.
Gipsa-lab supervises nearly 150 theses, including around 50 new ones each year. All the theses carried out in the laboratory are financed and supervised by teacher-researchers and researchers, including 50 holders of an HDR.
Finally, around sixty Master's trainees come each spring to swell the ranks of the laboratory.
The proposed PhD thesis work will be conducted in the ACTIV (Learning Classification Image and Video Processing) team of GIPSA-lab (Grenoble), with collaboration with the LIRIS laboratory in Lyon. The PhD student will be jointly advised by Dr. Kai Wang (thesis director, GIPSA-lab) and Prof. Florent Dupont (LIRIS). Working language can be either French or English.

Three-dimensional (3D) models, representing a person, animal, or mechanical object, are commonly used in various applications such as digital entertainment, virtual reality, and computer-aided design. Robust 3D watermarking is a promising technique for copyright protection of 3D models (mainly represented as 3D meshes). The inserted watermark should be invisible and robust to different processing operations on the watermarked 3D models. In the proposed thesis project, we aim to develop new and reliable methods for watermarking 3D models by leveraging the deep learning approach. A specially designed deep neural network is trained in an end-to-end manner to perform robust watermark insertion and extraction for 3D models. This replaces the manual design of watermarking features and functions, which is often laborious and generally suboptimal.
- First, we begin with a literature review that consists of a detailed reading and analysis of important and recent 3D mesh watermarking methods, as well as representative research in related fields such as deep-learning-based image watermarking and 3D shape analysis.
- Second, we would like to implement a specifically designed neural network to achieve robustness against the difficult connectivity attacks (which can radically change the number and position of mesh vertices while preserving the 3D shape).
- Third, in order to better adapt to usage scenarios of 3D models (e.g., also protecting the photometric content of models and being able to extract watermarks from 2D views of 3D models), we will conduct original studies on the interaction between different types of mesh information for watermark insertion and extraction.
- Qualified candidates should have a Master/Engineer degree in Computer Science or related fields.
- Experience in 3D model processing and/or machine learning will be a plus.
- Proficiency in Python programming.
- Excellent communication skills and strong motivation.