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

Organisation/Company

CNRS

Department

Laboratoire des Signaux et Systèmes

Research Field

Engineering
Computer science
Mathematics

Researcher Profile

First Stage Researcher (R1)

Application Deadline

10 Apr 2026 - 23:59 (UTC)

Country

France

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

35

Offer Starting Date

1 Sep 2026

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

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).

"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.

Website

https://emploi.cnrs.fr/Offres/Doctorant/UMR8506-STEDOU-020/Default.aspx

Research Field

Engineering

Education Level

PhD or equivalent

Research Field

Computer science

Education Level

PhD or equivalent

Research Field

Mathematics

Education Level

PhD or equivalent

Languages

FRENCH

Level

Basic

Research Field

Engineering

Years of Research Experience

None

Research Field

Computer science

Years of Research Experience

None

Research Field

Mathematics

Years of Research Experience

None

Website for additional job details

https://emploi.cnrs.fr/Offres/Doctorant/UMR8506-STEDOU-020/Default.aspx

Number of offers available

1

Company/Institute

Laboratoire des Signaux et Systèmes

Country

France

City

GIF SUR YVETTE

Geofield

City

GIF SUR YVETTE

Website

http://www.l2s.centralesupelec.fr/

• X (formerly Twitter)
• Facebook
• LinkedIn
• Whatsapp
• 
  More share options
  • E-mail
  • Pocket
  • Viadeo
  • Gmail
  • Weibo
  • Blogger
  • Qzone
  • YahooMail