134 citations · 736 across the 77 of their papers we have counts for
61 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…
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…
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…
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…
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…
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing
Ivan DeAndres-Tame, Muhammad Faisal, Ruben Tolosana +3
Face Recognition (FR) has advanced significantly with the development of deep learning, achieving high accuracy in several applications. However, the lack of interpretability of th…
Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss
Nahuel González, Giuseppe Stragapede, Rubén Vera-Rodriguez +1
In 2021, the pioneering work TypeNet showed that keystroke dynamics verification could scale to hundreds of thousands of users with minimal performance degradation. Recently, the K…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
SDFR: Synthetic Data for Face Recognition Competition
Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George +25
Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advance…
How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability
Ivan DeAndres-Tame, Ruben Tolosana, Ruben Vera-Rodriguez +3
Large Language Models (LLMs) such as GPT developed by OpenAI, have already shown astonishing results, introducing quick changes in our society. This has been intensified by the rel…
Synthetic Data for the Mitigation of Demographic Biases in Face Recognition
Pietro Melzi, Christian Rathgeb, Ruben Tolosana +5
This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have th…
IEEE BigData 2023 Keystroke Verification Challenge (KVC)
Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana +26
This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD),…
Keystroke Verification Challenge (KVC): Biometric and Fairness Benchmark Evaluation
Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana +4
Analyzing keystroke dynamics (KD) for biometric verification has several advantages: it is among the most discriminative behavioral traits; keyboards are among the most common huma…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…
SaFL: Sybil-aware Federated Learning with Application to Face Recognition
Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez +2
Federated Learning (FL) is a machine learning paradigm to conduct collaborative learning among clients on a joint model. The primary goal is to share clients' local training parame…