132 citations · 906 across the 96 of their papers we have counts for
108 papers
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs
Gonzalo Mancera, Julian Fierrez, Aythami Morales +3
This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine whether specific samples we…
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…
Mobile Interaction for Assessing Fatigue, Sleep, and Activity in Neurodegenerative and Chronic Diseases
Julian Fierrez, Alejandro Peña, Aythami Morales +13
Fatigue, sleep, or disturbances in daily activities are common symptoms among patients with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID). The…
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…
AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations
Javier Irigoyen, Roberto Daza, Francisco Jurado +5
We present AIriskEval-edu Demo, a platform that audits the pedagogical quality of instructional explanations and provides explainable audit results. The platform evaluates an expla…
CrimeNER Demo: Named-Entity Recognition in the Crime Domain
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
We present CrimeNER Demo, an AI-powered platform that enables us to extract general crime-related information from documents and classify them into entity types with two levels of…
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…
Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond
Javier Irigoyen, Roberto Daza, Aythami Morales +5
The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this impera…
AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations
Javier Irigoyen, Roberto Daza, Francisco Jurado +5
This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content…
Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setup
Roberto Daza, Javier Irigoyen, Ivan Lopez +5
We introduce AIriskEval-gaming, an open platform and dataset to evaluate social engineering risks in LLM-mediated multimodal interaction through controlled games. It supports human…
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…
Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
Gonzalo Mancera, Daniel DeAlcala, Aythami Morales +3
We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP…
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…
Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening
Diego Jimenez-Oviedo, Ruben Vera-Rodriguez, Ruben Tolosana +2
Early detection of atypical cognitive-motor development is critical for timely intervention, yet traditional assessments rely heavily on subjective, static evaluations. The integra…
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…
CrimeNER: Curated Dataset and Methods for Research and Investigation in Language Intelligence
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales +7
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named…
EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations
Javier Irigoyen, Roberto Daza, Aythami Morales +4
This work introduces EduEVAL-DB, a dataset based on teacher roles designed to support the evaluation and training of automatic pedagogical evaluators and AI tutors for instructiona…
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…