activity
20192026
most citedDeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation

61 citations · 518 across the 94 of their papers we have counts for

collaborators

110 papers

cs.CV2026

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…

cs.CV2026

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…

cs.HC2026

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…

cs.AI2026

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…

cs.CV2026

Unraveling Machine Behavior by Multi-Level Bias Analysis and Detection: Methodology and Application to Computer Vision

Ignacio Serna, Aythami Morales, Julian Fierrez

This study investigates the presence and propagation of bias within Neural Networks through a comprehensive multi-level analysis spanning the learned latent space, layer activation…

cs.CV2026

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…