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Hannes Mareen is a Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, specifically within the Department of Electronics and information systems (EA06), and is also affiliated with IMEC. His research spans multimedia security, image and video processing, and digital forensics, with a focus on watermarking techniques, deepfake detection, and media authentication. Dr. Mareen's research interests primarily focus on multimedia forensics and digital content security . His work addresses critical challenges in media authentication, including developing robust watermarking techniques that withstand geometric transformations, creating methods for deepfake video detection using advanced vision transformer architectures, and building tools for identifying AI-generated image manipulations. He has made significant contributions to perceptual hashing techniques and the analysis of compression fingerprints for forgery detection. His research has practical applications in copyright protection, content authentication, and combating misinformation through advanced media analysis tools. The recent publication trends reveal a strong focus on leveraging deep learning and vision transformers for multimedia security applications. Dr. Mareen's work demonstrates increasing sophistication in addressing complex challenges like geometrically robust watermarking, deepfake detection in video content, and the development of comprehensive datasets for training forgery detection systems. His research bridges theoretical advances with practical implementations, particularly evident in his work on real-time video delivery systems and applications in sports rehabilitation through virtual reality. Dr. Mareen has established a productive research trajectory with consistent high-impact publications in top conferences and journals. His collaborative network is extensive, with frequent co-authorship with Glenn Van Wallendael and Peter Lambert, indicating strong research group dynamics within Ghent University's multimedia security research team. His work spans both theoretical contributions and practical implementations, with several publications demonstrating real-time systems and applications.

• Multimedia Forensics
• Image and Video Watermarking
• Deepfake Detection
• +6 more

B.S. Manjunath is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). He serves as Director of the Center for Multimodal Big Data Science and Healthcare and the Center for Bio-image Informatics. His research focuses on image/video analysis, multimedia databases, steganography, and bio-informatics. Manjunath holds a Ph.D. from the University of Southern California (1991), M.E. from Indian Institute of Science (1987), and B.E. from Bangalore University (1985). He is an IEEE Fellow and ACM Fellow. Education: Ph.D. (1991), Signal & Image Processing Institute, EE-Systems, USC M.E. (1987), Systems Science and Automation, Indian Institute of Science B.E. (1985), Electronics Engineering, Bangalore University Research Interests: Image/video analysis (segmentation, registration, texture analysis) Multimedia databases and data mining Steganography and digital forensics Signal/image processing for bio-informatics His work includes developing the BisQue open-source platform for image informatics and leading NSF-funded initiatives in bio-image informatics and interactive multimedia. Key Contributions: Over 300 peer-reviewed publications 24 patented technologies Co-editor of the ISO/MPEG-7 multimedia standard Founder faculty of the Media Arts and Technology Program Awards and Affiliations: IEEE Fellow (2025) and ACM Fellow (2025) Affiliated with UCSB’s Computer Science Department and Dynamical Neuroscience Program Former Director of the NSF-sponsored Interactive Digital Multimedia IGERT program Labs and Platforms: Director of the Vision Research Lab Lead developer of the BisQue image informatics system Pioneering work in multimodal camera networks and healthcare analytics

• Image/Video Analysis
• Multimedia Databases
• Steganography
• +3 more

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at the University of Durham. He holds a Ph.D. in Computer Graphics from City University of Hong Kong and has held academic roles at The Hong Kong Polytechnic University prior to his current position. His research focuses on Computer Graphics, Machine Learning, Visual Aesthetics, and Educational Technologies. He serves as an Associate Editor for Frontiers in Education and Virtual Reality & Intelligent Hardware, and has organized major conferences such as ICWL 2022 and ISVC 2021. Education: B.A. (Hons.) in Computing Studies from The Hong Kong Polytechnic University, M.Phil. in Computer Science, and Ph.D. in Computer Graphics. Research interests include mesh saliency, human-object interaction recognition, cloud modeling, and student performance analytics. He has published extensively in top venues like IEEE Transactions, ACM SIGGRAPH, and ECCV. Notable awards include Best Paper (ITiCSE 2014) and Outstanding Paper (ICALT 2013). He currently chairs the Undergraduate Board of Examiners and serves as an external examiner for Northumbria University's MSc program. His work spans educational technologies, collaborative virtual environments, and visual aesthetics, with applications in VR therapy, face forgery detection, and artistic style transfer. He supervises numerous Ph.D. students and contributes to Durham’s academic governance, including curriculum development and postgraduate management.

• Computer Graphics
• Machine Learning and Deep Learning
• Geometric Modelling and Processing
• +5 more

Zitong Yu is an Assistant Professor of Computer Science at Great Bay University, China, leading the YU Vision (YUV) Group. He holds a PhD in Computer Science from the University of Oulu and has conducted postdoctoral research at Nanyang Technological University. His research focuses on computer vision, biometric security, and multimodal learning, with notable contributions to face anti-spoofing, deepfake detection, and remote physiological measurement. Postdoctoral Researcher: ROSE Lab, Nanyang Technological University (NTU) Visiting Scholar: University of Oxford (2021) His work bridges theoretical advancements with real-world applications, such as healthcare advisory roles with BioTrillion, USA. Key achievements include organizing international conferences (e.g., IJCB'24 Special Session), serving as Area Chair for BMVC 2024, and contributing to influential datasets like GenFace and DOLOS. Research interests span facial presentation attack detection, multimodal learning, and generative models for security. He has received awards including the World's Top 2% Scientists (2023-2024) and the Chinese Government Award for Outstanding Self-financed Students Abroad (2021). Grants include the Natural Science Foundation of China's Young Scientists Fund (300k RMB) and the Guangdong Provincial Regional Joint Fund (300k RMB). His labs and teams focus on advancing trustworthy AI systems through robust and generalizable models.

• Computer Vision
• Biometric Security
• Multimodal Learning
• +3 more

Vishnu Monn is an Associate Professor at Monash University Malaysia's School of IT, serving as Deputy Head of Education and Director of the Advanced Computing Platform. He holds a PhD in Engineering from Multimedia University (2016) and has over 15 years of academic and industry experience, including roles at Panasonic R&D Centre Malaysia (2005–2009) and Multimedia University (2009–2017). His research focuses on high-performance computing, predictive analytics, machine learning, computer vision, and soft robotics, with over MYR 1 million in secured grants as principal investigator. Education: B.Eng. (First-Class Honours) in Electrical and Electronics Engineering (2004) M.Eng. in Electrical and Electronics Engineering (2007) Ph.D. in Engineering (2016) Research interests include: High-performance computing architectures and applications Predictive analytics for industrial and environmental systems Machine learning models for computer vision tasks Soft robotics control systems Recent projects span autonomous driving algorithms, IoT-enabled smart cities, and soft robotics using reinforcement learning. He has published 61+ peer-reviewed articles in top journals and conferences, with recent work emphasizing multimodal learning, generative models, and blockchain optimization. Scientific contributions include leadership in interdisciplinary research, securing major grants, and establishing Monash Malaysia's high-performance computing facility. Teaching commitments include courses on big data, parallel computing, and embedded systems, with roles as chief examiner and program coordinator.

• High-performance computing
• Predictive analytics
• Machine learning & Artificial Intelligence
• +7 more

Lorenzo Seidenari is an Associate Professor of Computer Engineering at the Department of Information Engineering, University of Florence . He earned his PhD in Computer Engineering (2012) and MEng in Artificial Intelligence (2008) from the University of Florence. As a member of the UNIMORE ELLIS Unit, he contributes to European AI excellence, and co-founded the startup Small Pixels for video processing solutions. PhD in Computer Engineering, University of Florence (2012) MEng in Artificial Intelligence, University of Florence (2008) His research focuses on deep learning applications in computer vision and robotics , particularly generative AI , model uncertainty , and robustness . Recent work includes trajectory forecasting for autonomous systems, 360° video streaming optimization, and synthetic data generation. Key projects like WILD.AI and IMRA-SD address real-world challenges in biodiversity monitoring and multi-agent prediction. Recent publications highlight trends in generative adversarial networks (GANs) , spatio-temporal modeling , and autonomous driving applications. He has received the ELLIS Scholar award and holds habilitations from MIUR for associate professorship in both Computer Science (01/B1) and Computer Engineering (09/H1). As a conference area chair for ACM MM and BMVC, he actively shapes AI research discourse.

• Computer Vision
• Deep Learning
• Generative AI
• +4 more

Aythami Morales is a Professor at Universidad Autónoma de Madrid (UAM), supported by the Madrid Government under the V PRICIT program for Excellence in University Teaching Staff. His work bridges machine learning with real-world applications in auditable AI tools, security, privacy, and discrimination-aware learning . Key Research Themes: Biometrics, multimodal AI, fairness in machine learning, dietary assessment, and attention mechanisms Recent Projects: M2RAI (Multimodal and Multitask Responsible AI, 250K euros + PhD funding) and eMIRIAM (Risk Assessment in AI for Mental Health) His 15 most recent articles (2025) focus on AI auditability, biometric verification, and privacy-preserving techniques , spanning conferences like IEEE/IJCB, ICCV, and CVPR. He received the 2024 Stanford Top 2% Scientists in AI recognition and a Best Paper Award at CVPR 2025 . Morales actively engages in societal outreach through media (El País Retina) and public events like Pint of Science, addressing ethical AI and bias.

• Machine Learning
• Artificial Intelligence
• Biometrics
• +6 more

Kalin Stefanov is an ARC DECRA Fellow and Research Fellow in the Department of Human Centred Computing at Monash University. He holds a PhD in Computer Science from KTH Royal Institute of Technology and an MSc in Artificial Intelligence from the University of Amsterdam. His research focuses on Affective Computing, exploring systems that recognize and simulate human affects, with applications in social robotics, neurodiverse communication, and multimodal interaction. He has led projects on sign language translation and large-scale deepfake detection datasets. Key collaborations include work at the University of Southern California’s Institute for Creative Technologies and National Institute of Informatics. He has received accolades such as the Discovery Early Career Researcher Award (2023) and Best Paper Awards (2019, 2024). His research also contributes to UN SDG 4 (Quality Education) through accessible technologies for neurodiverse groups and visually impaired learners. Projects include the 'Active Generation of fingerspelling in Australian Sign Language' and 'Research Towards automated Australian Sign Language translation,' funded by the Australian Research Council. Stefanov’s work spans AI ethics, multimodal data platforms (e.g., OpenSense), and systems for social signal processing in human-robot interaction.

• Affective Computing
• Artificial Intelligence
• Accessible Computing
• +5 more

Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)

• Video Coding
• Immersive Media
• Digital Watermarking
• +5 more

Serkan Özbay serves as an Associate Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, Turkey. With academic appointments since 2003, he progressed from Lecturer to his current position (2024), maintaining continuous affiliation with the institution where he also completed all his degrees. His career demonstrates deep institutional integration and vertical advancement within the same department. His academic credentials were entirely earned at Gaziantep University: Doctorate (2007-2015): Institute of Science, Electrical and Electronic Engineering Master's (2003-2006): Institute of Science, Electrical and Electronics Engineering (Thesis) Licence (1997-2002): Faculty of Engineering, Electrical and Electronics Engineering Özbay's research centers on signal processing and machine learning applications, with significant contributions to computer vision for medical diagnostics (sinus segmentation, lung cancer detection) and security systems (image/video forgery analysis). His work bridges theoretical algorithms with practical implementations in edge computing and biomedical devices, evidenced by projects like real-time fall detection systems and metamaterial antennas. Recent publications show intensified focus on deep learning since 2020, particularly convolutional neural networks for medical imaging and object tracking. His publication trajectory reveals strategic interdisciplinary expansion: early work (2007-2015) concentrated on spectrum sensing and wireless communications, while post-2020 output pivoted toward AI-driven medical applications and digital forensics. Approximately 70% of his 22 publications (2019-2023) involve deep learning implementations, with strong institutional collaboration patterns—85% co-authored with Gaziantep University researchers. Journal publications predominantly appear in Q2-Q4 SCI-indexed engineering journals, while conference papers target international venues in computer vision and biomedical engineering. As an academic advisor, Özbay has supervised 16 graduate theses (1 PhD, 15 Master's) between 2017-2025, with 11 completed in 2020-2023 alone. Current advisees include students developing Raspberry Pi-based plate recognition systems and real-time fall detection solutions. He additionally mentors 15 undergraduate students in senior design projects annually, as evidenced by 2024 EEE498/499 project meetings. Administrative leadership includes Deputy Head of Department (2016), Erasmus Coordinator (2015-2016), and Institute Board membership (2020-2023).

• Electrical and Electronics Engineering
• Signal Processing
• Computer Vision
• +4 more