The FAR-SEE project aims to study the issues of sampling bias, fairness, uncertainty and explicability of these features for Artificial Intelligence (AI)-based face recognition systems, with the aim of improving existing algorithms, revealing 'optimal' performance/fairness/explicability trade-offs and thus formulating the principles of operational regulation of these systems. The objectives are therefore manifold. 1) To develop methods for detecting/correcting selection biases during learning (described not only by 'sensitive' variables such as gender or age, but also by the physiognomy of individuals and image characteristics, e.g. brightness), for ensuring fairness (an acceptable level of performance disparity between 'sensitive groups') without deteriorating performance, and for assessing the uncertainty involved in measuring performance and fairness metrics. 2) Explain the nature of proven sampling biases, the uncertainty inherent in performance/equity measurement, and the level of inequity measured, so as to be able to improve the methods developed to achieve objective 1). 3) In the light of the trade-offs between uncertainty, performance, fairness and explicability, describe the nature of acceptable/operational regulatory constraints that reconcile the constraints to be met by facial recognition systems. The project brings together three complementary partners with long-standing collaborative experience. It will draw on the expertise of IDEMIA's R&D team in facial recognition technologies and its knowledge of regulatory issues, the skills of the LTCI laboratory at Télécom Paris in the field of trustworthy AI, and those of the I3 laboratory in questions of ethics and operational regulation of AI.

After over a decade of research on AI security and despite major and regular advances in the state of the art, there are still significant limitations when it comes to protecting complex real-world systems, particularly highly dynamic and distributed systems such as those based on federated learning (FL). AI.MMUNITY aims to address two major challenges to secure these systems. First, using the MLOps formalism and modeling, the project seeks to expand threat modeling to consider the entire lifecycle of an FL system and not just the models. The goal is to characterize advanced threats that exploit the very broad attack surface of an FL system, including data and/or model poisoning attacks, attacks targeting the aggregation of local models or their deployment, and attacks on software (SW) or hardware (HW) implementations. Secondly, AI.MMUNITY focuses on "security by design" through a holistic approach based on three levels: at the system level with the reinforcement of operations and processes considered as security blind spots in the FL life cycle; at the model level with model reinforcement learning techniques; and at the implementation level through the development of innovative SW and HW protections on advanced platforms (SoC, MCU, RISC-V). AI.MMUNITY will demonstrate its innovations through three use cases in the fields of IoT cybersecurity, facial recognition systems, and human activity recognition (HAR) IoT applications. The methods and tools developed within AI.MMUNITY will enable AI actors and security and standardization bodies to improve the evaluation and reduce the impact of risks associated with these distributed learning systems.

The rise in the manipulation of images and voice represents a potential threat for crimes including disinformation campaigns, security fraud, extortion, online crimes against children, crypto jacking or illicit markets. Deepfake techniques are openly described and widely available; generating deepfakes is easy, and their quality has considerably improved. Therefore, it is challenging to detect them by mere visual analysis. As a consequence, there is an increasing need for deepfake detection tools. While several methods achieve good error rates under controlled scenarios, no dedicated tools are available for criminalistics experts. The goal of APATE is to deliver state-of-the-art methods to detect deepfakes. Instead of a “one size fits all” tool, the project aims at providing a toolbox of complementary techniques, based on the audio or visual parts of the video, by exploiting either low-level or semantic information, or by combining them in a multimodal manner. Each tool will address a different family of deepfakes, and will come with documentation detailing the use-cases, the known bias, the validation framework and how the results can be interpreted. The consortium includes criminalistics experts from the French National Scientific Police Service (SNPS), ensuring that the proposed toolbox is usable, properly described, and processes efficiently actual deepfakes found in criminal cases. In addition, the literature on deepfake generation will be continuously reviewed and analysed, to ensure that datasets corresponding to the latest deepfake generation techniques are available for the partners; a special concern will be to avoid overfitting to the learning databases. The consortium includes three research laboratories (Centre Borelli at ENS Paris Saclay, EPITA, LIX at Ecole Polytechnique), the SNPS, and IDEMIA, world leader in biometric recognition.

The BioQOP project deals with an architecture where a facial recognition system is distributed on several nodes. The innovation is to train each of these nodes in such a way that: (1) Individually, each node has a weak distinguishability capability; (2) Taken as a whole, the performance of the system, once all results are gathered, is optimal in terms of biometric recognition accuracy (under the privacy constraints). Biometric data processing is realized with deep convolutional neural networks (CNNs). Thanks to property (1), an adversary who has access to one node is not able to effectively exploit the underlying CNN signature for classifying biometric data. We plan to measure privacy loss thanks to Differential Privacy techniques. Furthermore, during the BioQOP project, we want to design optimization techniques for accessing and querying costly resources – the cost here being the required differential privacy budget.

During the next Olympic Games in Paris 2024, France will face a major security challenge because of a series of sports events, relayed around the World, involving personalities and the public. The history of the Games and sports has unfortunately already left traces of painful events, which we have the responsibility not to allow to reproduce here. On the basis of a sociological study of these risks, and in order to answer this challenge, the GIRAFE project proposes to develop algorithmic crowd control solutions based on video streams covering all or part of the public areas. These algorithms will in particular be able to alert the authorities of areas where crowds can become of concern, to monitor the flow of crowds and to anticipate possible phenomena of congestion; but also to identify abnormal cases occurring within such crowds, such as suspicious strolling of an individual, a chase or the transport and abandonment of a baggage, and to track their perpetrators to a possible interpellation. The tools created by the project will be in the spirit of facilitating the intervention of law enforcement and optimizing the use of security personnel, whose resources are limited and critical for an event of this magnitude. The human operator will remain the sole decision-maker of the actions to be taken during a warning. The legal and societal aspects associated with these video treatments will be studied and taken into account, to ensure the respect of the French legal framework, the GDPR, and keep the festive spirit of the Games. The project's various innovative algorithms use complementary approaches to detect abnormal events and manage the movement of crowds, so as to ensure maximum detection of risk situations and to trace alerts as quickly as possible. The research of the project will be based on three main pillars: - the movements of crowds, to manage the flow and the abnormal behaviors within a very dense crowd (specific approach in very dense zone); - the detection of abnormal behaviors, of which the learning of scenes given in JOP 2024 will make it possible to identify cases out of the ordinary (generic approach); - detection of pedestrians and baggage, transverse to the two previous axes, since in addition to the case of abandoned parcels (specific approach in sparsely populated area) this axis will ensure the identification and monitoring of the suspect individual to his arrest by the police The algorithms resulting from these thematic axes will be integrated into a demonstrator that optimizes the real-time processing of these algorithms, ergonomic for the end users, and prioritizes the flow of video surveillance cameras classified at risk or as unusual. The demonstrator will also be connected to a "Command and Control" in order to present to the operator, in a clear and global way, the cases of crowd movements to manage. The whole will be tested under pre-operational conditions and will reach a sufficient level of maturity, TRL6, to allow its deployment and its evaluation on various Olympic sites or in various places of the city of Paris at the end of the project.