Thesis M/F "Video content security in a deep learning coding architecture"

Job Description

Thesis Subject

"Video content security in a deep learning coding architecture"
Over the past few decades, numerous video compression algorithms have been developed, most based on a hybrid architecture combining transform coding and predictive coding. Standards such as H.264/AVC, HEVC, and VVC follow this principle. While they offer highly efficient compression performance, each module relies on a rigid, manual design. Furthermore, these modules cannot be jointly optimized end-to-end.

In parallel, recent years have seen the resounding success of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021, Li 2021, Quach 2022, Chen 2025]. Notably, several solutions have demonstrated competitive performance for video coding compared to traditional approaches.

In this emerging context of deep learning-based video coding, the objective of this thesis is to study the security of video content, particularly its confidentiality and integrity. Although solutions exist within the context of classical encoders [Dufaux 2008, Shahid 2011, Shahid 2014, Boyadjis 2017], to our knowledge, their application to these new encoders has not yet been explored in the literature and raises new challenges.

Initially, to preserve video confidentiality, we plan to study the encryption or obfuscation of variables in the latent space, after quantization but before the entropy coder. In this context, the PhD candidate will consider intradata, residual data, motion data, or a combination thereof. This approach ensures that the compressed bitstream can still be decoded, but with a noisy reconstructed video. To avoid a significant increase in bitrate, care must be taken to preserve the statistics of the latent variables. Since the latent space contains semantic information about the content, this approach has the potential to enable selective encryption of certain objects in the scene, for example, blurring faces in a video surveillance scenario. Secondly, we plan to explore content integrity verification. More specifically, the PhD candidate will study the use of a hash function in the latent space, combined with a digital signature. An attack is detected when the digital signature is missing or when the hash value is different from that decrypted from the compressed stream.

Your Work Environment

This thesis will be conducted within the MULTINET team of the Telecoms and Networks division of the Signals and Systems Laboratory (L2S).

The L2S (Signals and Systems Laboratory UMR 8506) is a joint research unit of the CNRS, CentraleSupélec, and Université Paris-Saclay, located in a restricted access zone. It comprises approximately 245 staff members, including 90 permanent staff, 10 technical and administrative staff, and 146 doctoral and postdoctoral researchers. It is organized into three research divisions (Signals & Statistics, Automation & Systems, and Telecoms & Networks) and three research support divisions (Human Resources and Communication, Financial Management, and IT).

The research conducted at the L2S focuses on the fundamental and applied mathematical aspects of control theory, signal and image processing, information theory, and communication. The position is located in a sector falling under the protection of scientific and technical potential (PPST), and therefore requires, in accordance with regulations, that your arrival be authorized by the competent authority of the Ministry of Higher Education, Research and Innovation (MESR).

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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