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Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy

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Multimedia Tools and Applications 2025年第4期84卷 1945-1966页

作者： Romero-Tapiador, Sergio Tolosana, Ruben Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Espinosa-Salinas, Isabel Freixer, Gala Carrillo de Santa Pau, Enrique Ramírez de Molina, Ana Ortega-Garcia, Javier Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Madrid28049 Spain IMDEA Food Institute CEI UAM+CSIC Madrid28049 Spain

Maintaining a healthy lifestyle has become increasingly challenging in today’s sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases. All these resources are available in GitHub (https://***/BiDAlab/AI4Food-NutritionDB). © The Author(s) 2024.

关键词： Nutrition

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Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs

arXiv

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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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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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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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DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance 25th

DeepFakes Evolution: Analysis of Facial Regions and Fake Det...

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25th International Conference on pattern Recognition Workshops, ICPR 2020

作者： Tolosana, Ruben Romero-Tapiador, Sergio Fierrez, Julian Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics Universidad Autonoma de Madrid Madrid Spain

ISBN: (纸本)9783030688202

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the 1 st generation such as UADFV and FaceForensics++ up to the latest databases of the 2 nd generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both 1 st and 2 nd DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system. Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the 2 nd generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors. © 2021, Springer Nature Switzerland AG.

关键词： Fake detection

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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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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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