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arXiv 2025年

作者： Mancera, Gonzalo DeAlcala, Daniel Fierrez, Julian Tolosana, Ruben Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autónoma de Madrid Spain

This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if given data was used for training machine learning models, and this work focuses on its application to the domain of Natural Language Processing. Using gradient-based analysis, the MINT model identifies whether particular data samples were included during the language model training phase, addressing growing concerns about data privacy in machine learning. The method was evaluated in seven Transformer-based models and six datasets comprising over 2.5 million sentences, focusing on text classification tasks. Experimental results demonstrate MINT’s robustness, achieving AUC scores between 85% and 99%, depending on data size and model architecture. These findings highlight MINT’s potential as a scalable and reliable tool for auditing machine learning models, ensuring transparency, safeguarding sensitive data, and fostering ethical compliance in the deployment of AI/NLP technologies. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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MINT-Demo: Membership Inference Test Demonstrator

arXiv

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arXiv 2025年

作者： DeAlcala, Daniel Morales, Aythami Fierrez, Julian Mancera, Gonzalo Tolosana, Ruben Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid28049 Spain

We present the Membership Inference Test Demonstrator, to emphasize the need for more transparent machine learning training processes. MINT is a technique for experimentally determining whether certain data has been used during the training of machine learning models. We conduct experiments with popular face recognition models and 5 public databases containing over 22M images. Promising results, up to 89% accuracy are achieved, suggesting that it is possible to recognize if an AI model has been trained with specific data. Finally, we present a MINT platform as demonstrator of this technology aimed to promote transparency in AI training1 © 2025, CC BY-NC-ND.

关键词： Face recognition

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Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognition

arXiv

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arXiv 2025年

作者： Romero-Tapiador, Sergio Tolosana, Ruben Lacruz-Pleguezuelos, Blanca Marcos-Zambrano, Laura Judith Bazán, Guadalupe X. Espinosa-Salinas, Isabel Fierrez, Julian Ortega-Garcia, Javier de Santa Pau, Enrique Carrillo Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid Spain IMDEA Food CEI UAM+CSIC Madrid Spain

Automatic dietary assessment based on food images remains a challenge, requiring precise food detection, segmentation, and classification. Vision-Language Models (VLMs) offer new possibilities by integrating visual and textual reasoning. In this study, we evaluate six state-of-the-art VLMs (ChatGPT, Gemini, Claude, Moondream, DeepSeek, and LLaVA), analyzing their capabilities in food recognition at different levels. For the experimental framework, we introduce the FoodNExTDB, a unique food image database that contains 9,263 expert-labeled images across 10 categories (e.g., "protein source"), 62 subcategories (e.g., "poultry"), and 9 cooking styles (e.g., "grilled"). In total, FoodNExTDB includes 50k nutritional labels generated by seven experts who manually annotated all images in the database. Also, we propose a novel evaluation metric, Expert-Weighted Recall (EWR), that accounts for the inter-annotator variability. Results show that closed-source models outperform open-source ones, achieving over 90% EWR in recognizing food products in images containing a single product. Despite their potential, current VLMs face challenges in fine-grained food recognition, particularly in distinguishing subtle differences in cooking styles and visually similar food items, which limits their reliability for automatic dietary assessment. The FoodNExTDB database is publicly available at https://***/AI4Food/FoodNExtDB. © 2025, CC BY-NC-ND.

关键词： Dietary supplements

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OTB-morph:One-time biometrics via Morphing

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Machine Intelligence Research 2023年第6期20卷 855-871页

作者： Mahdi Ghafourian Julian Fierrez Ruben Vera-Rodriguez Aythami Morales Ignacio Serna Biometrics and Data Pattern Analytics Laboratory(BiDA Lab) Universidad Autonoma de MadridMadrid28049Spain

Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition *** transformation is repeatable enabling subsequent biometric *** paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization *** proposed scheme is based on time-varying keys(random biometrics in our case)and morphing *** experimental implementation of the proposed scheme is given for face *** results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.

关键词： biometrics face recognition template protection morphing security

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DeepSign: Deep On-Line Signature Verification

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on biometrics, Behavior, and Identity Science 2021年第2期3卷 229-239页

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Biometrics and Data Pattern Analytics-BiDA Lab Universidad Autonoma de Madrid Madrid Spain

Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered. The main contributions of this study are: i) we provide an in-depth analysis of state-of-the-art deep learning approaches for on-line signature verification, ii) we present and describe the new DeepSignDB on-line handwritten signature biometric public database,1 iii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and iv) we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)2. for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user.1https://***/bidalab/DeepSignDB 2Spanish Patent Application (P202030060). © 2019 IEEE.

关键词： Recurrent neural networks

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Exploring a Patch-Wise Approach for Privacy-Preserving Fake ID Detection

arXiv

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arXiv 2025年

作者： Muñoz-Haro, Javier Tolosana, Ruben Vera-Rodriguez, Ruben Morales, Aythami Fierrez, Julian Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid Spain

In an increasingly digitalized world, verifying the authenticity of ID documents has become a critical challenge for real-life applications such as digital banking, crypto-exchanges, renting, etc. This study focuses on the topic of fake ID detection, covering several limitations in the field. In particular, no publicly available data from real ID documents exists, and most studies rely on proprietary in-house databases that are not available due to privacy reasons. In order to shed some light on this critical challenge that makes difficult to advance in the field, we explore a trade-off between privacy (i.e., amount of sensitive data available) and performance, proposing a novel patch-wise approach for privacy-preserving fake ID detection. Our proposed approach explores how privacy can be enhanced through: i) two levels of anonymization for an ID document (i.e., fully- and pseudo-anonymized), and ii) different patch size configurations, varying the amount of sensitive data visible in the patch image. Also, state-of-the-art methods such as Vision Transformers and Foundation Models are considered in the analysis. The experimental framework shows that, on an unseen database (DLC-2021), our proposal achieves 13.91% and 0% EERs at patch and ID document level, showing a good generalization to other databases. In addition to this exploration, another key contribution of our study is the release of the first publicly available database that contains 48,400 patches from both real and fake ID documents, along with the experimental framework and models, which will be available in our GitHub1 © 2025, CC BY-NC-ND.

关键词： Anonymity

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Do you need more data? the DeepSignDB on-line handwritten signature biometric database 15

Do you need more data? the DeepSignDB on-line handwritten si...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Morales, Aythami Ortega-Garcia, Javier Biometrics and Data Pattern Analytics BiDA Lab Universidad Autonoma de Madrid Spain

ISBN: (纸本)9781728128610

data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular on-line signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub. © 2019 IEEE.

关键词： biometrics

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DeepFakesON-Phys: Deepfakes detection based on heart rate estimation

DeepFakesON-Phys: Deepfakes detection based on heart rate es...

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2021 Workshop on Artificial Intelligence Safety, SafeAI 2021

作者： Hernandez-Ortega, Javier Tolosana, Ruben Fierrez, Julian Morales, Aythami Biometrics and Data Pattern Analytics Lab BiDA Lab Universidad Autonoma de Madrid Spain

This work introduces a novel DeepFake detection framework based on physiological measurement. In particular, we consider information related to the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze video sequences looking for subtle color changes in the human skin, revealing the presence of human blood under the tissues. In this work we investigate to what extent rPPG is useful for the detection of DeepFake videos. The proposed fake detector named DeepFakesON-Phys uses a Convolutional Attention Network (CAN), which extracts spatial and temporal information from video frames, analyzing and combining both sources to better detect fake videos. DeepFakesON-Phys has been experimentally evaluated using the latest public databases in the field: Celeb-DF and DFDC. The results achieved, above 98% AUC (Area Under the Curve) on both databases, outperform the state of the art and prove the success of fake detectors based on physiological measurement to detect the latest DeepFake videos. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Heart

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SaFL: Sybil-Aware Federated Learning with Application to Face Recognition 31

SaFL: Sybil-Aware Federated Learning with Application to Fac...

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31st IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2024

作者： Ghafourian, Mahdi Fierrez, Julian Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics BiDA-Lab Spain

ISBN: (纸本)9798331515942

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 parameters with an integrating server while preserving their privacy. This method permits to exploit the potential of massive mobile users' data for the benefit of machine learning models' performance while keeping sensitive data on local devices. On the downside, FL raises security and privacy concerns that have just started to be studied. To address some of the key threats in FL, researchers have proposed to use secure aggregation methods (e.g. homomorphic encryption, secure multiparty computation, etc.). These solutions improve some security and privacy metrics, but at the same time bring about other serious threats such as poisoning attacks, backdoor attacks, and free running attacks. This paper proposes a new defense method against poisoning attacks in FL called SaFL (Sybil-Aware Federated Learning) that minimizes the effect of sybils with a novel time-variant aggregation scheme. © 2024 IEEE.

关键词： Federated learning

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DeepSignCX: Signature complexity detection using recurrent neural networks 15

DeepSignCX: Signature complexity detection using recurrent n...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Vera-Rodriguez, Ruben Tolosana, Ruben Caruana, Miguel Manzano, Gustavo Gonzalez-Garcia, Carlos Fierrez, Julian Ortega-Garcia, Javier Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics-BiDA Lab Madrid Spain

ISBN: (纸本)9781728128610

This paper proposes a novel approach for on-line signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). Three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the on-line signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). This initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. This complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance. © 2019 IEEE.

关键词： biometrics

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