Publications

2025

• Correct-by-construction requirement decomposition
  
  • Sun Minghui
  • Bakirtzis Georgios
  • Jafarzadeh Hassan
  • Fleming Cody
  Software and Systems Modeling, Springer Verlag, 2025, pp.1-16. In systems engineering, accurately decomposing requirements is crucial for creating well-defined and manageable system components, particularly in safety-critical domains. Despite the critical need, rigorous, top-down methodologies for effectively breaking down complex requirements into precise, actionable sub-requirements are scarce, especially compared to the wealth of bottom-up verification techniques. Addressing this gap, we introduce a formal decomposition for contract-based design that guarantees the correctness of decomposed requirements if specific conditions are met. Our (semi-)automated methodology augments contract-based design with reachability analysis and constraint programming to systematically identify, verify, and validate sub-requirements representable by continuous bounded sets---continuous relations between real-valued inputs and outputs. We demonstrate the efficacy and practicality of a correct-by-construction approach through a comprehensive case study on a cruise control system, highlighting how our methodology improves the interpretability, tractability, and verifiability of system requirements. (10.1007/s10270-025-01291-4)
  DOI : 10.1007/s10270-025-01291-4
• Securing Cooperative Vehicular Platooning with a Set of Reinforced Checks
  
  • Braiteh Farah-Emma
  • Bassi Francesca
  • Khatoun Rida
  , 2025. Platooning enhances road safety and alleviates traffic congestion by enabling vehicles to travel closely together and maneuver in a coordinated manner. This coordination is facilitated by vehicle-to-vehicle (V2V) communications, which, unfortunately, also expose the platoon to potential cyberattack risks. In this paper, we present a novel platoon joining protocol, with a particular emphasis on the enrollment phase. We demonstrate that an attacker can disrupt the platoon’s formation or stability by falsely joining, without actually maneuvering into the platoon. To mitigate this risk, we propose robust physical challenges and data-consistency countermeasures that reinforce both the stability and integrity of the platoon. Simulations using Plexe validate the security of the designed protocol, as verified and confirmed thorough security checks. (10.1109/IWCMC65282.2025.11059583)
  DOI : 10.1109/IWCMC65282.2025.11059583
• Interactive Sketch-based Modeling of Braided Hair
  
  • Jetti Hari Hara Gowtham
  • Parakkat Amal Dev
  , 2025, pp.1-2. Hair braids are widely used in various games and animated movies, thanks to their simplified representation and ease of animation. However, the existing research on modeling braids often relies on a limited dictionary of commonly seen hair braid patterns, constraining artists' ability to experiment by creating imaginary or creative hair braids. In this paper, we introduce a simple sketch-based interface for creating arbitrary hair braids. Our method employs a two-stage framework that first interprets a user-drawn sketch to extract the braid pattern. To accommodate arbitrarily drawn sketches, we then use a physics-inspired simulation to generate visually pleasing braids. In addition to automatically generating braids, our system allows users to interactively refine the braid pattern to create braids that match the user's imagination, facilitating experimentation and exploration of different braid structures. (10.2312/egp.20251027)
  DOI : 10.2312/egp.20251027
• Multi-client Functional Encryption with Public Inputs and Strong Security
  
  • Nguyen Ky
  • Phan Duong Hieu
  • Pointcheval David
  , 2025, 15676, pp.68-101. Recent years have witnessed a significant development for functional encryption (FE) in the multi-user setting, particularly with multi-client functional encryption (MCFE). The challenge becomes more important when combined with access control, such as attribute-based encryption (ABE), which was actually not covered syntactically by the public-key FE nor semantically by the secret-key MCFE frameworks. On the other hand, as for complex primitives, many works have studied the admissibility of adversaries to ensure that the security model encompasses all real threats of attacks. 1. At a conceptual level, by adding a public input to FE/MCFE, we cover many previous primitives, notably attribute-based function classes. Furthermore, with the strongest admissibility for inner-product functionality, our framework is quite versatile, as it encrypts multiple sub-vectors, allows repetitions and corruptions, and eventually also encompasses public-key FE and classical ABE, bridging the private setting of MCFE with the public setting of FE and ABE. 2. Finally, we propose an MCFE with public inputs with the class of functions that combines inner-products (on private inputs) and attribute-based access-control (on public inputs) for LSSS policies. We achieve the first AB-MCFE for inner products with strong admissibility (from Nguyen et al., ACNS’23) and with adaptive security. In the end, our concrete MCFE leads to MIFE for inner products, public-key single-input inner- product FE with LSSS key-policy, and KP-ABE for LSSS, with adaptive security. Previous AB-MCFE constructions are either restricted in terms of weaker admissibility (Nguyen et al., ASIACRYPT’22) or considers a slightly larger functionality of attribute-weighted sum but with only selective security (Agrawal et al., CRYPTO’23). (10.1007/978-3-031-91826-1_3)
  DOI : 10.1007/978-3-031-91826-1_3
• Infusion: Internal Diffusion for Inpainting of Dynamic Textures and Complex Motion
  
  • Cherel Nicolas
  • Almansa Andrés
  • Gousseau Yann
  • Newson Alasdair
  , 2023, pp.446-450. Video inpainting is the task of filling a desired region in a video in a visually convincing manner. It is a very challenging task due to the high dimensionality of the signal and the temporal consistency required for obtaining convincing results. Recently, diffusion models have shown impressive results in modeling complex data distributions, including images and videos. Diffusion models remain nonetheless very expensive to train and perform inference with, which strongly restrict their application to video. We show that in the case of video inpainting, thanks to the highly auto-similar nature of videos, the training of a diffusion model can be restricted to the video to inpaint and still produce very satisfying results. This leads us to adopt an internal learning approch, which also allows for a greatly reduced network size. We call our approach "Infusion": an internal learning algorithm for video inpainting through diffusion. Due to our frugal network, we are able to propose the first video inpainting approach based purely on diffusion. Other methods require supporting elements such as optical flow estimation, which limits their performance in the case of dynamic textures for example. We introduce a new method for efficient training and inference of diffusion models in the context of internal learning. We split the diffusion process into different learning intervals which greatly simplifies the learning steps. We show qualititative and quantitative results, demonstrating that our method reaches state-of-the-art performance, in particular in the case of dynamic backgrounds and textures. (10.1111/cgf.70070)
  DOI : 10.1111/cgf.70070
• Arithmetisation of the Floor Function and Its Applications to Homomorphic Cryptography
  
  • Berthet Pierre-Augustin
  • Tavernier Cédric
  , 2025. Cryptography has historically been based on integer arithmetic. Thus, there was no need to investigate functions related to real numbers or analysis, such as the fractional part or the floor function. The floor function has several applications in modern cryptography. Its arithmetisation can allow for the application of generic side-channel countermeasures, like masking, without being limited by the chosen representation of rationnal or real numbers. It has also some applications in Fully Homomorphic Encryption (FHE), either directly in CKKS, or indirectly, as an arithmetised floor function can be computed with FHE. A consequence is the possibility of protecting normalisation or discretisation operations in Machine Learning or Deep Learning. In this work, we perform the arithmetisation by adapting a Fourier series and speeding up its convergence by composing partial series with themselves.
• PerceptualLift: Using hatches to infer a 3D organic shape from a sketch
  
  • Butler Tara
  • Guehl Pascal
  • Parakkat Amal Dev
  • Cani Marie-Paule
  , 2025. In this work, we investigate whether artistic hatching, popular in pen-and-ink sketches, can be consistently perceived as a depth cue. We illustrate our results by presenting PerceptualLift, a modeling system that exploits hatching to create curved 3D shapes from a single sketch. We first describe a perceptual user study conducted across a diverse group of participants, which confirms the relevance of hatches as consistent clues for inferring curvature in the depth direction from a sketch. It enables us to extract geometrical rules that link 2D hatch characteristics, such as their direction, frequency, and magnitude, to the changes of depth in the depicted 3D shape. Built on these rules, we introduce PerceptualLift, a flexible tool to model 3D organic shapes by simply hatching over 2D hand-drawn contour sketches. (10.2312/exw.20251055)
  DOI : 10.2312/exw.20251055
• Memory attacks in network nonlocality and self-testing
  
  • Weilenmann Mirjam
  • Budroni Costantino
  • Navascués Miguel
  Quantum, Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften, 2025, 9, pp.1735. We study what can or cannot be certified in communication scenarios where the assumption of independence and identical distribution (iid) between experimental rounds fails. In this respect, we prove that membership tests for non-convex sets of correlations cannot be formulated in the non-iid regime. Similarly, it is impossible to self-test non-extreme quantum operations, such as mixed states, or noisy quantum measurements, unless one allows more than a single use thereof within the same experimental round. One consequence of our results is that non-classicality in causal networks without inputs cannot be experimentally demonstrated. By analyzing optimal non-iid strategies in the triangle scenario, we raise the need to take into account the prior communication required to set up a causal network. (10.22331/q-2025-05-06-1735)
  DOI : 10.22331/q-2025-05-06-1735
• Digital Persuasion: Understanding the Impact of Online Influencers on Public Opinion
  
  • Berjawi Omran
  • Khatoun Rida
  • Fenza Giuseppe
  , 2025.
• Anamorphism Beyond One-to-One Messaging: Public-Key with Anamorphic Broadcast Mode
  
  • Do Xuan Thanh
  • Persiano Giuseppe
  • Phan Duong Hieu
  • Yung Moti
  , 2025, 15603, pp.429-455. To date, Anamorphic Cryptography [EC22] has been developed to support adding an anamorphic message within a ciphertext carrying a primary message. The anamorphic message remains hidden even in the presence of a strong adversary that possesses the receiver’s key and/or determined the sent primary message. In this paper, we expand one-to-one encrypted anamorphic communication to one-to-many anamorphism, naturally assuming communication over a broadcast channel. What we show is that using a previously designed public-key encryption scheme, two things can happen: First, the receiver of an added hidden message may be a party different from the actual receiver (i.e., a shadow party) who has initially collaborated with the sender. Secondly, and perhaps more surprisingly, the receiving party need not be a singleton, and can be a number of different shadow (i.e., anonymous) groups, each receiving a different anamorphic message, where all these messages are extracted from a single one-receiver ciphertext. The idea of having multiple hidden channels to different shadow groups is highly handy if, for example, the anamorphic messages are warnings with operational instructions, sent to the groups and will be received by a group even if the adversary is able to temporarily cut off all but one members of a channel. More specifically, First, we motivate and formalize the notion of Public-Key Encryption with an Anamorphic Broadcast Mode. We then present, as an initial result of an independent interest, the first lattice-based construction of Anonymous Multi-Channel Broadcast Encryption. It is important to note here that all Multi-Channel Broadcast schemes to date are in the pairing-based setting (and are, thus, insecure against quantum adversaries). Finally, we show how to transform a strong form of anonymity (where the ciphertext also hides the number of channels) into a system with anamorphism in the multi-channel broadcast setting for the well-known Dual Regev Public-Key Encryption scheme. Specifically, we show that, given the public key for the Dual Regev encryption scheme, and a sequence of messages for the channels of broadcast scheme, it is possible to create a ciphertext that will carry the messages and is also a legitimate ciphertext for PK. (10.1007/978-3-031-91131-6_15)
  DOI : 10.1007/978-3-031-91131-6_15
• The syzygy distinguisher
  
  • Randriambololona Hugues
  , 2025, 15606, pp.324-354. We present a new distinguisher for alternant and Goppa codes, whose complexity is subexponential in the error-correcting capability, hence better than that of generic decoding algorithms. Moreover it does not suffer from the strong regime limitations of the previous distinguishers or structure recovery algorithms: in particular, it applies to the codes used in the Classic McEliece candidate for postquantum cryptography standardization. The invariants that allow us to distinguish are graded Betti numbers of the homogeneous coordinate ring of a shortening of the dual code. Since its introduction in 1978, this is the first time an analysis of the McEliece cryptosystem breaks the exponential barrier. (10.1007/978-3-031-91095-1_12)
  DOI : 10.1007/978-3-031-91095-1_12
• Active Bipartite Ranking with Smooth Posterior Distributions
  
  • Cheshire James
  • Clémençon Stephan
  , 2025, Volume 258: International Conference on Artificial Intelligence and Statistics. <div><p>In this article, bipartite ranking, a statistical learning problem involved in many applications and widely studied in the passive context, is approached in a much more general active setting than the discrete one previously considered in the literature. While the latter assumes that the conditional distribution is piece wise constant, the framework we develop permits in contrast to deal with continuous conditional distributions, provided that they fulfill a Hölder smoothness constraint. We first show that a naive approach based on discretisation at a uniform level, fixed a priori and consisting in applying next the active strategy designed for the discrete setting generally fails. Instead, we propose a novel algorithm, referred to as smooth-rank and designed for the continuous setting, which aims to minimise the distance between the ROC curve of the estimated ranking rule and the optimal one w.r.t. the sup norm. We show that, for a fixed confidence level ε &gt; 0 and probability δ ∈ (0, 1), smooth-rank is PAC(ε, δ). In addition, we provide a problem dependent upper bound on the expected sampling time of smooth-rank and establish a problem dependent lower bound on the expected sampling time of any PAC(ε, δ) algorithm. Beyond the theoretical analysis carried out, numerical results are presented, providing solid empirical evidence of the performance of the algorithm proposed, which compares favorably with alternative approaches.</p></div>
• VRSurf: Surface Creation from Sparse, Unoriented 3D Strokes
  
  • Sureshkumar Anandhu
  • Parakkat Amal Dev
  • Bonneau Georges-Pierre
  • Hahmann Stefanie
  • Cani Marie-Paule
  Computer Graphics Forum, Wiley, 2025, 44 (2). Although intuitive, sketching a closed 3D shape directly in an immersive environment results in an unordered set of arbitrary strokes, which can be difficult to assemble into a closed surface. We tackle this challenge by introducing VRSurf, a surfacing method inspired by a balloon inflation metaphor: Seeded in the sparse scaffold formed by the strokes, a smooth, closed surface is inflated to progressively interpolate the input strokes, sampled into lists of points. These are treated in a divide-and-conquer manner, which allows for automatically triggering some additional balloon inflation followed by fusion if the current inflation stops due to a detected concavity. While the input strokes are intended to belong to the same smooth 3D shape, our method is robust to coarse VR input and does not require strokes to be aligned. We simply avoid intersecting strokes that might give an inconsistent surface position due to the roughness of the VR drawing. Moreover, no additional topological information is required, and all the user needs to do is specify the initial seeding location for the first balloon. The results show that VRsurf can efficiently generate smooth surfaces that interpolate sparse sets of unoriented strokes. Validation includes a side-by-side comparison with other reconstruction methods on the same input VR sketch. We also check that our solution matches the user's intent by applying it to strokes that were sketched on an existing 3D shape and comparing what we get to the original one. (10.1111/cgf.70071)
  DOI : 10.1111/cgf.70071
• Differentially Private Policy Gradient
  
  • Rio Alexandre
  • Barlier Merwan
  • Colin Igor
  , 2025. Motivated by the increasing deployment of reinforcement learning in the real world, involving a large consumption of personal data, we introduce a differentially private (DP) policy gradient algorithm. We show that, in this setting, the introduction of Differential Privacy can be reduced to the computation of appropriate trust regions, thus avoiding the sacrifice of theoretical properties of the DP-less methods. Therefore, we show that it is possible to find the right trade-off between privacy noise and trust-region size to obtain a performant differentially private policy gradient algorithm. We then outline its performance empirically on various benchmarks. Our results and the complexity of the tasks addressed represent a significant improvement over existing DP algorithms in online RL.
• Price of Safety in Linear Best Arm Identification
  
  • Shang Xuedong
  • Colin Igor
  • Barlier Merwan
  • Cherkaoui Hamza
  , 2025. We introduce the safe best-arm identification framework with linear feedback, where the agent is subject to some stage-wise safety constraint that linearly depends on an unknown parameter vector. The agent must take actions in a conservative way so as to ensure that the safety constraint is not violated with high probability at each round. Ways of leveraging the linear structure for ensuring safety has been studied for regret minimization, but not for best-arm identification to the best our knowledge. We propose a gap-based algorithm that achieves meaningful sample complexity while ensuring the stage-wise safety. We show that we pay an extra term in the sample complexity due to the forced exploration phase incurred by the additional safety constraint. Experimental illustrations are provided to justify the design of our algorithm.
• HISTOIRESMORALES: A French Dataset for Assessing Moral Alignment
  
  • Leteno Thibaud
  • Proskurina Irina
  • Gourru Antoine
  • Velcin Julien
  • Laclau Charlotte
  • Metzler Guillaume
  • Gravier Christophe
  , 2025, pp.2590–2612. Aligning language models with human values is crucial, especially as they become more integrated into everyday life. While models are often adapted to user preferences, it is equally important to ensure they align with moral norms and behaviours in real-world social situations. Despite significant progress in languages like English and Chinese, French has seen little attention in this area, leaving a gap in understanding how LLMs handle moral reasoning in this language. To address this gap, we introduce HISTOIRESMORALES, a French dataset derived from MORALSTORIES, created through translation and subsequently refined with the assistance of native speakers to guarantee grammatical accuracy and adaptation to the French cultural context. We also rely on annotations of the moral values within the dataset to ensure their alignment with French norms. HISTOIRESMORALES covers a wide range of social situations, including differences in tipping practices, expressions of honesty in relationships, and responsibilities toward animals. To foster future research, we also conduct preliminary experiments on the alignment of multilingual models on French and English data and the robustness of the alignment. We find that while LLMs are generally aligned with human moral norms by default, they can be easily influenced with user-preference optimization for both moral and immoral data. (10.48550/arXiv.2501.17117)
  DOI : 10.48550/arXiv.2501.17117
• Differentially Private Deep Model-based Rein-forcement Learning
  
  • Rio Alexandre
  • Barlier Merwan
  • Colin Igor
  , 2025. We address private deep offline reinforcement learning (RL), where the goal is to train a policy on standard control tasks that is differentially private (DP) with respect to individual trajectories in the dataset. To achieve this, we introduce PRIMORL, a model-based RL algorithm with formal differential privacy guarantees. PRIMORL first learns an ensemble of trajectory-level DP models of the environment from offline data. It then optimizes a policy on the penalized private model, without any further interaction with the system or access to the dataset. In addition to offering strong theoretical foundations, we demonstrate empirically that PRIMORL enables the training of private RL agents on offline continuous control tasks with deep function approximations, whereas current methods are limited to simpler tabular and linear Markov Decision Processes (MDPs). We furthermore outline the tradeoffs involved in achieving privacy in this setting.
• EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics
  
  • Wan Sky Chenwei
  • Labeau Matthieu
  • Clavel Chloé
  , 2025, pp.1678-1695. Designing emotionally intelligent conversational systems to provide comfort and advice to people experiencing distress is a compelling area of research. Recently, with advancements in large language models (LLMs), end-to-end dialogue agents without explicit strategy prediction steps have become prevalent. However, implicit strategy planning lacks transparency, and recent studies show that LLMs’ inherent preference bias towards certain socio-emotional strategies hinders the delivery of high-quality emotional support. To address this challenge, we propose decoupling strategy prediction from language generation, and introduce a novel dialogue strategy prediction framework, EmoDynamiX, which models the discourse dynamics between user fine-grained emotions and system strategies using a heterogeneous graph for better performance and transparency. Experimental results on two ESC datasets show EmoDynamiX outperforms previous state-of-the-art methods with a significant margin (better proficiency and lower preference bias). Our approach also exhibits better transparency by allowing backtracing of decision making. (10.18653/v1/2025.naacl-long.81)
  DOI : 10.18653/v1/2025.naacl-long.81
• Adaptive Sample Sharing for Multi Agent Linear Bandits
  
  • Cherkaoui Hamza
  • Barlier Merwan
  • Colin Igor
  , 2025. The multi-agent linear bandit setting is a well-known setting for which designing efficient collaboration between agents remains challenging. This paper studies the impact of data sharing among agents on regret minimization. Unlike most existing approaches, our contribution does not rely on any assumptions on the bandit parameters structure. Our main result formalizes the trade-off between the bias and uncertainty of the bandit parameter estimation for efficient collaboration. This result is the cornerstone of the Bandit Adaptive Sample Sharing (BASS) algorithm, whose efficiency over the current state-of-the-art is validated through both theoretical analysis and empirical evaluations on both synthetic and real-world datasets. Furthermore, we demonstrate that, when agents' parameters display a cluster structure, our algorithm accurately recovers them.
• Wild SBOMs: a Large-scale Dataset of Software Bills of Materials from Public Code
  
  • Soeiro Luı́s
  • Robert Thomas
  • Zacchiroli Stefano
  , 2025. Developers gain productivity by reusing readily available Free and Open Source Software (FOSS) components. Such practices also bring some difficulties, such as managing licensing, components and related security. One approach to handle those difficulties is to use Software Bill of Materials (SBOMs). While there have been studies on the readiness of practitioners to embrace SBOMs and on the SBOM tools ecosystem, a large scale study on SBOM practices based on SBOM files produced in the wild is still lacking. A starting point for such a study is a large dataset of SBOM files found in the wild. We introduce such a dataset, consisting of over 78 thousand unique SBOM files, deduplicated from those found in over 94 million repositories. We include metadata that contains the standard and format used, quality score generated by the tool sbomqs, number of revisions, filenames and provenance information. Finally, we give suggestions and examples of research that could bring new insights on assessing and improving SBOM real practices.
• Does Functional Package Management Enable Reproducible Builds at Scale? Yes
  
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  , 2025. Reproducible Builds (R-B) guarantee that rebuilding a software package from source leads to bitwise identical artifacts. R-B is a promising approach to increase the integrity of the software supply chain, when installing open source software built by third parties. Unfortunately, despite success stories like high build reproducibility levels in Debian packages, uncertainty remains among field experts on the scalability of R-B to very large package repositories. In this work, we perform the first large-scale study of bitwise reproducibility, in the context of the Nix functional package manager, rebuilding 709 816 packages from historical snapshots of the nixpkgs repository, the largest cross-ecosystem open source software distribution, sampled in the period 2017-2023. We obtain very high bitwise reproducibility rates, between 69 and 91% with an upward trend, and even higher rebuildability rates, over 99%. We investigate unreproducibility causes, showing that about 15% of failures are due to embedded build dates. We release a novel dataset with all build statuses, logs, as well as full "diffoscopes": recursive diffs of where unreproducible build artifacts differ.
• Piecewise NARX Behavioral Model for RF Power Amplifiers in 5G Applications
  
  • Pham Thuy T.
  • Pham Dang-Kièn Germain
  • Mohellebi Reda
  • Almairac Pierre
  • Pedrosa Carolina
  • Desgreys Patricia
  , 2025. <div><p>The complexity of RF PA behavior in 5G communication systems is driven by nonlinearities and long memory effects under wideband, high dynamic range signals; this poses significant challenges for existing models. Traditional Volterrabased models, such as the Generalized Memory Polynomial (GMP), struggle with overfitting, while neural network-based approaches require large datasets and exhibit high computational complexity both for training and inference. This paper presents a novel Piecewise Nonlinear AutoRegressive with eXogenous inputs (PW-NARX) model that combines the strengths of piecewise modeling and the NARX architecture to capture both nonlinear and memory effects efficiently over high dynamic ranges. Each sub-model in the piecewise framework operates within a different region of the input space, significantly reducing model complexity while maintaining high accuracy. Simulation results demonstrate that the PW-NARX model outperforms state-of-the-art models, achieving the lowest normalized mean square error (NMSE) of -39.18 dB and similar or better NMSE performance as other state-of-the-art models with fewer parameters.</p></div>
• Théorie ondulatoire statistique
  
  • Badeau Roland
  , 2025. La théorie ondulatoire statistique établit formellement les lois statistiques vérifiées par les solutions de l’équation des ondes, dans un domaine connexe et borné de l’espace. Elle constitue ainsi la solution mathématique d’un problème très ancien en acoustique des salles, qui a fait couler beaucoup d’encre depuis les travaux pionniers de Wallace Clement Sabine à la fin du XIXe siècle : l’étude du phénomène de réverbération.Elle fournit notamment l’expression analytique de la distribution de puissance et des corrélations du champ acoustique par rapport au temps, la fréquence et la position dans l’espace, en fonction de la géométrie de la salle et des conditions aux limites. Elle nous permet par exemple de retrouver et d’améliorer, dans le cas particulier d’un champ acoustique isotrope, les formules du temps de réverbération originalement établies par Sabine et Eyring, ainsi que la formule de corrélation spatiale. Mais elle s’applique également à des formes géométriques pouvant engendrer un champ acoustique anisotrope.Notre objectif sera ici de présenter cette théorie de la manière la plus simple et intuitive possible, en l’abordant sous un angle purement géométrique. Nous montrerons ainsi que deux chemins mathématiques très différents, le premier basé sur l’asymptotique de Weyl et les billards mathématiques, le second basé sur la géométrie et la cristallographie, convergent vers les mêmes conclusions, ce qui nous rend extrêmement confiants quant à la validité scientifique de cette théorie. Nous fournirons également la confirmation expérimentale de certaines prédictions de la théorie, qui vont au-delà des propriétés statistiques déjà connues de la réverbération.
• ROSA: Finding Backdoors with Fuzzing
  
  • Kokkonis Dimitri
  • Marcozzi Michaël
  • Decoux Emilien
  • Zacchiroli Stefano
  , 2025, pp.2816-2828. A code-level backdoor is a hidden access, programmed and concealed within the code of a program. For instance, hard-coded credentials planted in the code of a file server application would enable maliciously logging into all deployed instances of this application. Confirmed software supply chain attacks have led to the injection of backdoors into popular open-source projects, and backdoors have been discovered in various router firmware. Manual code auditing for backdoors is challenging and existing semi-automated approaches can handle only a limited scope of programs and backdoors, while requiring manual reverse-engineering of the audited (binary) program. Graybox fuzzing (automated semi-randomized testing) has grown in popularity due to its success in discovering vulnerabilities and hence stands as a strong candidate for improved backdoor detection. However, current fuzzing knowledge does not offer any means to detect the triggering of a backdoor at runtime. In this work we introduce ROSA, a novel approach (and tool) which combines a state-of-the-art fuzzer (AFL++) with a new metamorphic test oracle, capable of detecting runtime backdoor triggers. To facilitate the evaluation of ROSA, we have created ROSARUM, the first openly available benchmark for assessing the detection of various backdoors in diverse programs. Experimental evaluation shows that ROSA has a level of robustness, speed and automation similar to classical fuzzing. It finds all 17 authentic or synthetic backdooors from ROSARUM in 1h30 on average. Compared to existing detection tools, it can handle a diversity of backdoors and programs and it does not rely on manual reverse-engineering of the fuzzed binary code. (10.1109/ICSE55347.2025.00183)
  DOI : 10.1109/ICSE55347.2025.00183
• Computer vision and halftone visual culture: improving similarity search for historical photographs
  
  • Aissi Mohamed Salim
  • Giardinetti Marina
  • Bloch Isabelle
  • Schuh Julien
  • Foliard Daniel
  Multimedia Tools and Applications, Springer Verlag, 2025, pp.1-21. This article advances a method to analyze a large corpus of historical photographs using artificial intelligence tools and data modeling. This research was conducted within the framework of the EyCon (Early Conflict Photography 1890-1918 and Visual AI) and HighVision projects, which aim at leveraging the power of digital tools, exploiting both visual and textual information, to investigate the development of war photography at the turn of the 20th century. To do so, one of the objectives of the project was to develop a method to extract robust features and to overcome the challenges posed by the halftone printing techniques, the most common way to reproduce photographs in daily newspapers, periodicals and books at the time. By combining visual and textual similarity measures, the proposed approach enables the identification of significant subsets of similarity within the dataset. The findings from this research hold important implications for the broader field of image analysis and provide insights into the unique characteristics and complexities of historical visual data. This work contributes to the advancement of computer vision techniques in the analysis of historical photographic collections, opening up new avenues for research in visual AI and archival studies. (10.1007/s11042-025-20855-6)
  DOI : 10.1007/s11042-025-20855-6