132 citations · 906 across the 96 of their papers we have counts for
72 papers · 1 filter
Forensic Twins: Self-Supervised Residual Learning for AI-Generated Image Forensics
Javier Muñoz-Haro, Ruben Tolosana, Ruben Vera-Rodriguez +2
Detectors of AI-generated images are typically trained using samples from all Generative AI architectures they must catch, and struggle as soon as a new architecture emerges. Recen…
Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V
Pablo Lozano-Jimenez, Sergio Romero-Tapiador, Ruben Tolosana
We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The proposed method is built as fami…
VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
Luis F. Gomez, Julian Fierrez, Roberto Daza +5
Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments h…
FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking
Javier Muñoz-Haro, Muñoz-Haro Javier, Andres Teruel +11
Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI models can now inject localiz…
Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering
Miguel Lopez-Duran, Elena Marrero, Julian Fierrez +8
Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vi…
EERLoss: A Novel Loss Function for Training Deep Biometric Models. A Case Study in Keystroke Dynamics
Nahuel Gonzalez, Marta Robledo-Moreno, Ivan DeAndres-Tame +2
Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary…
Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2
Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez +3
We present the Membership Inference Test (MINT) Demo 2, a framework designed to improve transparency in machine learning training processes. MINT is a technique for experimentally…
Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study
Marta Robledo-Moreno, Ruben Vera-Rodriguez, Ruben Tolosana +1
Recent advancements in Vision-Language Models (VLMs) have demonstrated strong capabilities in general visual reasoning, yet their applicability to rigorous biometric tasks remains…
Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark
Laura Pedrouzo-Rodriguez, Luis F. Gomez, Ruben Tolosana +4
Recent advances in photorealistic avatar generation have enabled highly realistic talking-head avatars, raising security concerns regarding identity impersonation in AI-mediated co…
Exploring Deep Learning and Ultra-Widefield Imaging for Diabetic Retinopathy and Macular Edema
Pablo Jimenez-Lizcano, Sergio Romero-Tapiador, Ruben Tolosana +4
Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness among working-age adults. Traditional approaches in the literature focus on s…
Membership Inference Test: Auditing Training Data in Object Classification Models
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +2
In this research, we analyze the performance of Membership Inference Tests (MINT), focusing on determining whether given data were utilized during the training phase, specifically…
Is Visual Realism Enough? Evaluating Gait Biometric Fidelity in Generative AI Human Animation
Ivan DeAndres-Tame, Chengwei Ye, Ruben Tolosana +2
Generative AI (GenAI) models have revolutionized animation, enabling the synthesis of humans and motion patterns with remarkable visual fidelity. However, generating truly realisti…
Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning
Daniel DeAlcala, Aythami Morales, Julian Fierrez +3
Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a no…
AttZoom: Attention Zoom for Better Visual Features
Daniel DeAlcala, Aythami Morales, Julian Fierrez +1
We present Attention Zoom, a modular and model-agnostic spatial attention mechanism designed to improve feature extraction in convolutional neural networks (CNNs). Unlike tradition…
Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos
Laura Pedrouzo-Rodriguez, Pedro Delgado-DeRobles, Luis F. Gomez +4
Photorealistic talking-head avatars are becoming increasingly common in virtual meetings, gaming, and social platforms. These avatars allow for more immersive communication, but th…
AG-VPReID 2025: Aerial-Ground Video-based Person Re-identification Challenge Results
Kien Nguyen, Clinton Fookes, Sridha Sridharan +20
Person re-identification (ReID) across aerial and ground vantage points has become crucial for large-scale surveillance and public safety applications. Although significant progres…
Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics
Imanol Solano, Julian Fierrez, Aythami Morales +4
Demographic bias in high-performance face recognition (FR) systems often eludes detection by existing metrics, especially with respect to subtle disparities in the tails of the sco…
Comparison of Visual Trackers for Biomechanical Analysis of Running
Luis F. Gomez, Gonzalo Garrido-Lopez, Julian Fierrez +4
Human pose estimation has witnessed significant advancements in recent years, mainly due to the integration of deep learning models, the availability of a vast amount of data, and…
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +3
The automatic analysis of document layouts in digital-born PDF documents remains a challenging problem due to the heterogeneous arrangement of textual and nontextual elements and t…
Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance
Simone Maurizio La Cava, Roberto Casula, Sara Concas +5
3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to the limits and characteristics of the different application scenarios. In this study, we inve…
Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognition
Sergio Romero-Tapiador, Ruben Tolosana, Blanca Lacruz-Pleguezuelos +7
Automatic dietary assessment based on food images remains a challenge, requiring precise food detection, segmentation, and classification. Vision-Language Models (VLMs) offer new p…
FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection
Javier Muñoz-Haro, Ruben Tolosana, Ruben Vera-Rodriguez +2
Verifying the authenticity of identity documents (IDs) has become a critical challenge for real-life applications such as digital banking, crypto-exchanges, renting, etc. This stud…
MINT-Demo: Membership Inference Test Demonstrator
Daniel DeAlcala, Aythami Morales, Julian Fierrez +3
We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determ…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…