Eva Giboulot is an Inria researcher in Rennes, as a member of the Artishau Team.. She received the Ph.D. Degree in Engineering science from the University of Technology of Troyes, France with research activities in information security and signal processing, focusing on steganography and steganalysis. She served as a Post-Doc Researcher at the Artificial Intelligence Center of the Czech Technical University, Prague, Czechia. Her current interests are in the area of AI security, particularly in the design of secure watermarking methods as well as theoretically grounded forensics methods for the detection of generated content. She is a member of the IEEE Information Forensics and Security Technical Committee since 2025.

Watermarking is the secure communication of a mark over an unreliable channel. This tutorial aims to present a general methodology for designing inference-based text watermarking. In particular, the goal is to teach how to construct watermarking schemes which possess explicit theoretical guarantees in terms of probability of false alarm and, when possible, in terms of detectability. We start with the design of a powerful, yet naïve, approach: the KGW algorithm. From this baseline we touch upon the most common design mistakes of watermarking in the literature: the use of z-scores, insecure designs and poorly defined quality metrics. To address these issues, we construct a general watermarking framework based on statistical hypothesis testing and information theory. We illustrate the framework with a plug-and-play zero-bit scheme, WaterMax, as well as a tentative multi-bit one. We conclude with the limitations of inference-based schemes, notably the difficulty of using them in an open-source setting, as well as potential remedies: weight-based schemes and finetuning.

This course addresses the verification and certification of vision-based landing systems in aeronautics under the ARP AI standard framework. Participants will explore robust testing via adversarial attacks, robust validation, and formal verification tailored to object detection metrics like IoU. The curriculum covers formal explainability for failure hazard analysis and provides hands-on Colab tutorials using PyTorch, auto_LiRPA solvers, and the LARD dataset to deliver actionable industrial certification assets.

Maximilian Kiener is Head of the Institute for Ethics in Technology at Hamburg University of Technology (tuhh.de/ethics) and Associate Research Fellow at the Oxford Uehiro Institute for Practical Ethics. He holds a BPhil and DPhil in Philosophy from the University of Oxford, where he also served as an Extraordinary Junior Research Fellow and Leverhulme Early Career Fellow. His research explores the ethics of artificial intelligence, with a particular emphasis on ethics by design and moral responsibility.

This talk presents deep ethics as an emerging field concerned with translating qualitative ethical reasoning into computational forms, including metrics, models, and decision procedures. Deep ethics promises to embed ethical reasoning directly into AI design. But it also risks excluding what cannot be measured, disguising assumptions as technical facts, and narrowing practical judgement. The central question is whether ethics can be made computable without losing the openness and contestability that make ethical deliberation possible.