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Christophe Charrier is a Full Professor in Forensics and AI at Université de Caen Normandie, affiliated with GREYC UMR CNRS 6072 and IUT Grand Ouest Normandie's Multimedia and Internet Department (Dept. MMI). He obtained his PhD in Computer Science from Université Jean Monnet (Saint-Etienne) in 1998, followed by an HDR (Habilitation à Diriger des Recherches) in 2011 from Université de Caen Normandie. His academic journey includes roles as a Postdoctoral Researcher at Université Laval (1998-2001), Associate Professor at IUT Saint-Lô (2001), and Visiting Scholar/Professor positions at University of Texas at Austin (2008) and University of Sherbrooke (2009-2011). His research focuses on Digital Image and Video Forensics (e.g., deepfake detection), Image/Video Quality Assessment , Computational Vision , and Biometrics (fingerprint quality, template update, presentation attack detection). He leads the SAFE research group since 2016 and collaborates with the E-payment & Biometrics team at GREYC. His work integrates machine learning for quality metrics, biometric system evaluation, and forensic analysis. Recent publications highlight advancements in deepfake detection , 3D mesh quality assessment , and biometric security . Articles span journals like Intelligent Service Robotics (2024), IEEE Access (2024), and conferences such as CORESA (2024) and Cyberworlds (2023-2024). His studies on fingerprint systems, behavioral biometrics, and environmental impacts on data quality underscore his interdisciplinary approach. Scientific Awards : Best PhD Paper Award (ASONAM 2022) Best Full Paper Award (CW2022) He has mentored 14 PhD students since 2003, including notable alumni like Xinwei Liu (Zhejiang Wanli University) and Antoine Cabana (ALTEN, Toulouse). His projects span biometric certification, latent space manipulation, and 3D mesh evaluation, often in collaboration with institutions in Canada, Norway, and Morocco.

• Digital Image Forensics
• Video Forensics
• Biometrics
• +7 more

Jean-Christophe Burie is a Professor at the University of La Rochelle, serving as Vice-President for Digital Campus and Information Systems, Deputy Director of the L3i Laboratory (Informatics, Imaging, and Interaction), and Director of the joint research laboratory SAIL (Sequential Art Image Laboratory). His interdisciplinary work bridges computer science and humanities. His research focuses on image processing, pattern recognition, and artificial intelligence , with applications in historical manuscripts, comics indexing, and digital security. Key projects include enhancing palm-leaf manuscript analysis (AMADI/STIC ASIE), developing e-BDthèque for comics content extraction, and identity verification systems (MOBIDEM/IDECYS). He collaborates internationally with institutions like Leiden University and Vietnam's ICT Lab on medical imaging and cultural heritage preservation. He advises doctoral students in areas spanning ancient text analysis, document security, and manga character recognition. His recent publications demonstrate a strong emphasis on deep learning adaptations for document analysis, biometric security, and cross-modal recognition systems.

• Image Processing
• Pattern Recognition
• Machine Learning
• +6 more

Nicholas Evans is a Professor in the Digital Security department at EURECOM, where he teaches mathematical methods for engineers and speech and audio processing. He has been affiliated with EURECOM since October 2007 and leads research in speaker recognition, anti-spoofing, and biometric security. Previously, he was a Professor at the University of Wales Swansea (2002–2006) and taught at the University of Avignon (2006–2007). Research Interests: His work centers on biometric security, particularly in detecting spoofing and deepfake attacks in automatic speaker verification systems. Key areas include presentation attack detection, privacy-preserving voice technologies, and robust speech processing under real-world conditions. He actively contributes to advancing anti-spoofing countermeasures through large-scale datasets and challenge evaluations. The recent publications reflect a strong trend in developing and evaluating spoofing detection mechanisms, with a focus on real-world applicability, adversarial robustness, and multimodal analysis. His work spans from foundational feature engineering (e.g., Constant Q cepstral coefficients) to large-scale datasets like SpoofCeleb and ASVspoof, influencing both academic research and practical security systems. NeurIPS 2023 Scholar Award for 'StressID: a multimodal dataset for stress identification' Best Paper Award at IBERSPEECH 2022 Best System Award at IberSpeech 2018 Multiple Best Paper Awards (2016, 2017) Student Paper Award at WIFS 2013 Elected to IEEE Speech and Language Technical Committee (2013) Nicholas Evans has supervised numerous students and early-career researchers, many of whom are co-authors on his publications. He leads significant research initiatives such as the ASVspoof and SpoofCeleb challenges, which are supported by collaborative grants and institutional funding. His lab focuses on developing secure, privacy-preserving speech technologies with applications in biometrics and cybersecurity. He is a key member of international research teams and contributes to major challenges in voice privacy and spoofing detection, including the Voice Privacy Challenge and BIOSIG. His work involves close collaboration with institutions across Europe and Asia, and he maintains active profiles on Google Scholar, ResearchGate, and IEEE Xplore.

• Speaker Recognition
• Biometrics
• Anti-Spoofing
• +7 more

Professor Jean-Luc Dugelay is a faculty member at EURECOM's Digital Security Department, specializing in facial image processing, image forensics, and biometrics. He holds a PhD in Image Processing from the University of Rennes (1992) and is a Fellow of IEEE (2012) and IAPR (2018). His research focuses on security applications like deepfake detection, privacy protection, and multi-spectral imaging. He leads projects on UAV surveillance, cross-spectrum face recognition, and deepfake countermeasures. Education: PhD in Image Processing (1992, University of Rennes), HDR (2013, University of Nice). Notable projects include HEIMDALL (deepfake detection), CONVERGE (transport security), and ImVerif (image forensics). His work on biometric systems and digital watermarking has been recognized with awards like the SEE Blondel Medal (2010). Research interests span facial analysis, video surveillance, and thermal imaging. He has advised students on topics like age estimation via GANs and cross-spectrum face recognition. Key publications include work on deepfake detection (IPAS 2025), thermal-to-visible face conversion, and event-based vision systems.

• Biometrics
• Image Forensics
• Video Surveillance
• +7 more

Emmanuel Giguet is a Researcher at the French National Centre for Scientific Research (CNRS), affiliated with the GREYC Laboratory (Computer Science Department) at the University of Caen, Normandy, France. He holds an HDR (Accreditation to Supervise Research) from the University of Caen (2011) and a PhD in Computer Science (1998), both focusing on multilingual natural language processing and document analysis. Roles: Cybersecurity Researcher, Communication and Scientific Mediation Adviser (GREYC Lab), Former Computer Forensics Legal Expert (2005–2015). Expertise: Digital Forensics, Natural Language Processing, Document Structure Analysis, Open Source Intelligence, Competitive Intelligence. His research spans cybersecurity, digital forensics, and NLP applications in document analysis. He co-founded Semiotime (2012), a Competitive Intelligence startup, and has contributed to tools like the GREYC Digital Investigation Platform (G'DIP). He teaches cybersecurity modules at the University of Caen, emphasizing digital forensics and information retrieval. Key Projects: Development of open-source digital investigation tools (e.g., G'DIP). Analysis of deepfake videos and forensic video forgery detection. PDF document structure extraction for financial narrative processing (FinTOC). Affiliations: GREYC Lab (CNRS UMR 6072), University of Caen. Institute for Information Sciences, University of Caen. Communication: Advises GREYC on branding and media presence, including logo guidelines, virtual backgrounds, and scientific mediation resources.

• Computer Science
• Computer Forensics
• Natural Language Processing
• +4 more

Sam Goree is an assistant professor of computer science at Stonehill College, specializing in the intersection of computer vision, human-computer interaction, and digital humanities. His work critically examines how AI systems evaluate aesthetic quality and human-centered aspects of AI, with particular focus on subjectivity in machine learning models and evaluation methodologies. PhD in Informatics from Indiana University's Luddy School of Informatics, Computing and Engineering Goree's research explores aesthetic phenomenon problems in computer vision, particularly image aesthetic quality assessment. He investigates how subjective human perceptions of beauty and aesthetics can be modeled and evaluated in AI systems, challenging traditional approaches that assume a universal standard of beauty. His work draws from feminist theory, philosophy of science, and media archaeology to develop more human-centered evaluation methods for AI systems. Goree is particularly interested in how subjectivity manifests in both data and models, and how this affects algorithmic bias. His recent publications reveal a strong trend toward human-centered evaluation of AI systems, with increasing focus on subjectivity, personalization, and philosophical foundations of aesthetics in computer vision. Goree's work bridges technical computer vision research with critical humanities perspectives, examining how historical contexts shape current AI practices. He has developed novel evaluation frameworks that incorporate qualitative human feedback, moving beyond traditional quantitative metrics to better understand how AI systems interact with human perception and values. Goree actively engages with teaching challenges in the era of generative AI, developing pedagogical approaches that help students develop critical thinking skills while navigating new AI tools. His teaching philosophy emphasizes maintaining human-centered learning experiences even as AI transforms educational landscapes. He advocates for assignments that require contextual understanding and creativity, which are difficult for AI to replicate. His research is organized around understanding the "aesthetic gap" - the difference between information extractable from image pixels and the feelings images evoke in humans. Goree's lab explores how to build more transparent and accountable AI systems that acknowledge their subjective nature rather than claiming false objectivity.

• computer vision
• human-computer interaction
• digital humanities
• +4 more

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

• Visual Data Analysis
• Remote Sensing
• Steganography
• +10 more

Christophe Rosenberger is a full professor of Cybersecurity at ENSICAEN School of Engineering (University of Caen Normandy) and director of the GREYC research lab. He holds a PhD in Computer Science from the University of Rennes 1 (1999) and an HDR (2006). His research focuses on biometrics (keystroke dynamics, soft biometrics), digital forensics, and cybersecurity, with 250+ publications and 28 supervised PhD students. He leads projects like the PEPR Cybersecurity COMPROMIS initiative and chairs international conference tracks. Education: PhD (1999, University of Rennes 1), Master's (1996), and Bachelor's (1994) in Computer Science and Mathematics from the University of Rennes 1. Research interests include behavioral biometrics, phishing detection, and explainable AI. He has developed tools like GREYC-Hashing for biometric template protection and GREYC-Keystroke software for keystroke analysis. Awards include the 2023 highest grade promotion to professor and Asgard laureateships for Norway collaborations. Teaching includes courses on cybersecurity, machine learning, and digital identity at ENSICAEN. He co-supervises 3 ongoing PhD projects on biometric explainability, fairness evaluation, and phishing detection. Active in committees like the CNRS Computer Science Institute and the French-Norwegian MyTIGER program. Media engagements include radio/podcast appearances on biometrics and AI, featured in articles by CNRS, Le Figaro, and others. The GREYC lab, under his leadership, hosts over 200 researchers and collaborates globally on cybersecurity and AI initiatives.

• Cybersecurity
• Biometrics
• Digital Forensics
• +4 more

Patrick Bas serves as Research Director and Thematic Group Facilitator for the Data Intelligence Group (DatInG) at the University of Lille, France, where he leads the Signal and Images team within the CRISTAL laboratory. His office is located at ESPRIT in Lille's Scientific City, and he holds membership on the institution's Scientific Council. Bas earned his Electrical Engineering degree (1997) and Ph.D. in Signal and Image Processing (2000) from the Institut National Polytechnique de Grenoble. His academic journey includes postdoctoral work at Université Catholique de Louvain, CNRS research at Gipsa-Lab (2001-2009), and a visiting position at Helsinki University of Technology (2005-2008). His primary research centers on steganography, steganalysis, and digital watermarking, with significant contributions to image manipulation detection and neural network applications in multimedia security. He has organized major initiatives including the BOSS steganalysis contest (2010) and served as co-coordinator for the Ecrypt European NoE's watermarking virtual lab (2004-2008). While his Google Scholar profile indicates recent publications in graph theory and combinatorics (2021-2025), these appear inconsistent with his documented research focus on multimedia security as evidenced by his thesis supervision and professional activities. Bas has supervised seven doctoral theses including Rony Abecidan's work on robust image manipulation detection (2024) and Solène Bernard's research on neural network steganography (2021). He has held editorial roles for the Eurasip Journal on Information Security (since 2011) and IEEE Transactions on Information Forensics and Security (2013-2016), and co-organized the International Workshop on Information Hiding (IH07).

• Steganography
• Steganalysis
• Digital Watermarking
• +2 more

Giovanna Maria Dimitri is an Assistant Professor Tenure Track in Artificial Intelligence at Universitá degli Studi di Milano (Statale), with additional affiliations at the ICE, University of Cambridge, and the Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche (DIISM) at the University of Siena. She earned her PhD in Artificial Intelligence from the University of Cambridge under Prof. Pietro Liò, focusing on multilayer network methodologies for brain data analysis. She holds an MPhil in Advanced Computer Science from Cambridge with distinction and completed her Master’s and Bachelor’s in Computer and Automation Engineering at the University of Siena, both with top honors. PhD in Artificial Intelligence – University of Cambridge, UK MPhil in Advanced Computer Science – University of Cambridge, UK (Distinction) Master’s & Bachelor’s in Computer and Automation Engineering – University of Siena, Italy (110/110 cum laude) Her research spans a broad spectrum of artificial intelligence, including foundational models, deep learning, brain data modeling, and applications in healthcare, environmental science, and sustainability. She is particularly known for her work on GAN detection, emotional image datasets, climate change impact modeling, and AI for Sustainable Development Goals. Her interdisciplinary approach integrates computer science with neuroscience, public health, and social impact. Her recent publications reflect a strong trend in applying AI to real-world problems such as healthcare diagnostics (e.g., Brugada Syndrome detection), environmental monitoring (air quality, climate change on agriculture), and ethical AI (CO2 emissions of ML models). She also contributes to digital humanities and science communication, indicating a commitment to societal engagement and interdisciplinary collaboration. She has received the competitive Ai-Net Fellows Scholarship from DAAD in 2023, enabling collaboration with Prof. Gemma Roig’s lab. She is an Associate Editor for Neurocomputing (Elsevier) and was elected Associate Editor of IEEE Transactions on Technology and Society in May 2024. Dimitri has extensive teaching experience, lecturing Business Intelligence at the University of Siena and serving as a Guest Lecturer in Data Science at the University of Cambridge’s Institute of Continuing Education. She has supervised numerous students and has a publication record of nearly 60 peer-reviewed papers. She is also active in science communication, having been interviewed by Italian media and appearing on Rai Radio 1. She is a life member of Clare Hall College, University of Cambridge, and continues to contribute to academic and public discourse on AI through seminars, workshops, and editorial leadership.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +12 more

Nesma HOUMANI is a Lecturer at Telecom SudParis, part of the SAMOVAR research team. Her work focuses on biomedical signal processing, neuroimaging, and biometric authentication systems. She has contributed to Alzheimer’s disease detection via EEG analysis, gait quality measurement for neurological disorders, and neurofeedback applications for cognitive enhancement. Education: PhD in Biomedical Signal Processing (2011) from National Institute of Telecommunications (Institut National de Télécommunications) Research Interests: EEG-based diagnostics for neurological disorders Biometric systems (online signatures, gait analysis) Neurofeedback and neuromodulation Machine learning for medical signal analysis Recent Article Trends: Her recent work (2020-2025) emphasizes clinical applications of signal processing in Alzheimer’s detection, gait deviation measurement for hemiparesis patients, and neurofeedback for cognitive reserve enhancement in the elderly. Collaborations span interdisciplinary teams in neuroscience, engineering, and medicine. Patents: "Method for generating information about the production of a handwritten, hand-affixed or printed trace" (US 11989979B2, 2024) "Identity verification method using handwritten signatures on digital sensors" (EP3942442, 2022) Grants & Labs: Active in SAMOVAR’s projects on biomedical signal analysis and neurotechnology. Involved in multidisciplinary initiatives like the BV2 wellbeing project for chronic disease patients.

• Biomedical Engineering
• Signal Processing
• Neuroscience
• +5 more

Prof. Sonia Garcia is a faculty member at Telecom SudParis, holding the academic rank of Professor. Her primary research focuses on biometric analysis, medical signal processing, and machine learning applications in healthcare contexts. She has made significant contributions to understanding gait abnormalities in neurological disorders, handwriting analysis for early Alzheimer's detection, and enhancing security in biometric systems. Her work often integrates deep learning and statistical methods to analyze complex medical and behavioral data. Prof. Garcia has authored over 50 peer-reviewed articles and holds multiple patents related to identity verification via handwritten signatures and gait analysis. Her research spans collaborations with medical institutions and tech firms to develop practical solutions for clinical diagnostics and security systems. Notable contributions include the development of the OSIRIS iris recognition software and methodologies for quantifying gait asymmetry using advanced mathematical models. Her recent projects emphasize applying machine learning to predict treatment outcomes for neurological conditions and improving early detection of neurodegenerative diseases. This work has led to innovations in both algorithmic frameworks and biomedical engineering applications.

• Biometrics
• Medical Signal Processing
• Machine Learning
• +3 more

Massimiliano Todisco is a Lecturer in the Digital Security department at EURECOM, a leading research institute in digital sciences. His work is centered on enhancing the security and privacy of voice biometric systems, with a strong emphasis on detecting spoofing, deepfakes, and adversarial attacks in automatic speaker verification. His research interests include voice biometrics, adversarial machine learning, spoofing countermeasures, deepfake detection, and privacy-preserving speech technologies. He actively contributes to major international challenges such as ASVspoof and VoicePrivacy, driving innovation in secure and explainable biometric systems. The recent publications highlight a consistent focus on biometric security, with trends in adversarial attack modeling, voice anonymization, and large-scale evaluation of spoofing detection systems. His work bridges signal processing, machine learning, and cybersecurity. NeurIPS 2023 Scholar Award for “StressID: a multimodal dataset for stress identification” Best Paper Award at IBERSPEECH 2022 Best Paper Award in Computer Speech & Language (2017) Best Paper Award at ODYSSEY 2016 Todisco advises PhD students, as evidenced by his HDR (Habilitation à Diriger des Recherches) obtained in 2025, which qualifies him to supervise doctoral research. He has been involved in multiple collaborative research projects and challenges, including ASVspoof and VoicePrivacy, though specific grant details are not listed. His research is highly collaborative, often co-authored with researchers from institutions across Europe and beyond. He is a core member of the Digital Security research group at EURECOM, which serves as his primary research team and laboratory environment. This group focuses on cutting-edge challenges in digital security, particularly in biometrics and privacy.

• Voice Biometrics
• Automatic Speaker Verification
• Spoofing Detection
• +7 more

Yann Busnel is a Full Professor at IMT Atlantique in France, where he serves as Head of the Network Systems, Cyber Security and Digital Law (SRCD) department at the Rennes Campus since January 2017. He also leads the D2-NTS department (Networks, Telecommunications and Services) within UMR IRISA. His academic journey includes positions as Assistant Professor at Université de Nantes (2009-2014), Associate Professor and Head of Computer Science Department at ENSAI (2014-2016), and postdoctoral work at Università "La Sapienza" in Rome. Busnel's research focuses on models for large-scale distributed systems and networks, with particular expertise in data stream analysis over massive datasets. His work spans cybersecurity, dependability, medical data analysis (including pharmacovigilance and genomic sequence analysis), and the self-organized coordination of drone fleets for disaster management. He has published over 100 peer-reviewed papers and coordinated numerous national and international research projects. His recent scholarly output demonstrates a clear progression toward applied cybersecurity solutions, particularly in federated learning for intrusion detection, blockchain analysis, and wireless network security. The research shows strong interdisciplinary connections between distributed systems theory and practical security applications, with increasing emphasis on medical data analysis and drone coordination systems. His work bridges theoretical computer science with real-world security and networking challenges. Busnel actively mentors PhD students across multiple institutions, with current advisees working on topics including AI in cybersecurity using game theory, threat intelligence tools for IoT, and trust in distributed network services. He has coordinated substantial research funding through projects like CMA TCE (France 2030), EDIH Bretagne (Horizon Europe), and various ANR-funded initiatives. He leads several research chairs including OM&AI2MD (Operations Models and Artificial Intelligence to Manage Disasters), PRACom (Pôle de Recherche Avancée en Communications), and Cyber CNI (Cyber Security of Critical Infrastructures), demonstrating his leadership in both academic research and industry collaboration. His work with these chairs focuses on practical applications of his theoretical research in disaster management, communications technology, and critical infrastructure protection.

• Distributed Systems
• Networks
• Data Stream Analysis
• +9 more

Rémi Synave is an Associate Professor in the Department of Computer Science at the University of Strasbourg's Faculty of Science. His academic career spans over 15 years with continuous research output from 2006 to present, demonstrating sustained scholarly activity in computer graphics, 3D modeling, and interdisciplinary applications. Dr. Synave's research spans multiple domains with a strong focus on geometric algorithms, 3D reconstruction, and imaging applications. His work bridges computer science with anthropology, medicine, and cultural heritage preservation. Key research themes include geodesic path computation on triangular meshes, 3D surface reconstruction algorithms, medical imaging applications, and digital anthropology. His publications demonstrate consistent contributions to both theoretical computer graphics and practical applications across diverse fields. Analysis of his publication history reveals a strong trajectory from foundational geometric algorithms toward increasingly interdisciplinary applications. Early work focused on computational geometry and 3D reconstruction algorithms, while more recent publications demonstrate expansion into educational technology, image composition analysis, and digital humanities applications. His research consistently applies computer graphics techniques to solve domain-specific problems in anthropology, medicine, and cultural heritage. Rémi Synave maintains active collaborations with researchers across multiple institutions, particularly with Stefka Gueorguieva, Pascal Desbarats, and colleagues in anthropology and medical fields. His work on the Strasbourg Juvenile Collection represents a significant interdisciplinary contribution to digital anthropology. While specific teaching details aren't provided in the available materials, his publication on interdisciplinary arcade cabinet projects indicates innovative educational approaches. His laboratory work appears centered on 3D scanning, reconstruction, and visualization, with applications spanning from medical diagnostics to cultural heritage preservation. The consistent focus on algorithm development for geometric problems demonstrates a strong technical foundation applied across diverse practical contexts.

• Computer Graphics
• 3D Modeling and Reconstruction
• Image Processing
• +4 more