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OTB-morph:One-time biometrics via Morphing

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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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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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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How Good is ChatGPT at Face biometrics? A First Look into Recognition, Soft biometrics, and Explainability

arXiv

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arXiv 2024年

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

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 release of ChatGPT which allows anyone to interact in a simple conversational way with LLMs, without any experience in the field needed. As a result, ChatGPT has been rapidly applied to many different tasks such as code- and song-writer, education, virtual assistants, etc., showing impressive results for tasks for which it was not trained (zero-shot learning). The present study aims to explore the ability of ChatGPT, based on the recent GPT-4 multimodal LLM, for the task of face biometrics. In particular, we analyze the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results. ChatGPT could be very valuable to further increase the explainability and transparency of automatic decisions in human scenarios. Experiments are carried out in order to evaluate the performance and robustness of ChatGPT, using popular public benchmarks and comparing the results with state-of-the-art methods in the field. The results achieved in this study show the potential of LLMs such as ChatGPT for face biometrics, especially to enhance explainability. For reproducibility reasons, we release all the code in GitHub1 © 2024, CC BY-NC-ND.

关键词： Face recognition

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SaFL: Sybil-aware Federated Learning with Application to Face Recognition

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

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Image Processing Challenges (ICIPC), IEEE International Conference on

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

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

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.

关键词： Training Performance evaluation Metalearning Privacy Federated learning Prevention and mitigation Computational modeling Security Servers Protection

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SaFL: Sybil-aware Federated Learning with Application to Face Recognition

arXiv

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arXiv 2023年

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

关键词： Federated learning

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Mobile Keystroke biometrics Using Transformers 17

Mobile Keystroke Biometrics Using Transformers

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Stragapede, Giuseppe Delgado-Santos, Paula Tolosana, Ruben Vera-Rodriguez, Ruben Guest, Richard Morales, Aythami Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics Lab Spain School of Engineering University of Kent United Kingdom

ISBN: (纸本)9798350345445

Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our knowl-edge, this is the first study that proposes keystroke biometric systems based on Transformers. The proposed Transformer architecture has achieved Equal Error Rate (EER) values of 3.84% in the popular Aalto mobile keystroke database using only 5 enrolment sessions, outperforming by a large margin other state-of-the-art approaches in the literature. © 2023 IEEE.

关键词： biometrics

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BeCAPTCHA-Type: Biometric Keystroke data Generation for Improved Bot Detection

BeCAPTCHA-Type: Biometric Keystroke Data Generation for Impr...

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2023 IEEE/CVF Conference on Computer Vision and pattern Recognition Workshops, CVPRW 2023

作者： Dealcala, Daniel Morales, Aythami Tolosana, Ruben Acien, Alejandro Fierrez, Julian Hernandez, Santiago Ferrer, Miguel A. Diaz, Moises Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics Lab Spain University Las Palmas Gran Canaria Spain

ISBN: (纸本)9798350302493

This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. These approaches are validated on a bot detection task, using the keystroke synthetic data to improve the training process of keystroke-based bot detection systems. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects. We have analyzed the performance of the three synthesis approaches through qualitative and quantitative experiments. Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples. The experiments demonstrate the realism of the synthetic samples. The classification results suggest that in scenarios with large labeled data, these synthetic samples can be detected with high accuracy. However, if the proposed synthetic data is nor properly modelled using massive data by bot detectors, then that data will be very difficult to detect even for the most sophisticate bot detectors. Furthermore, these results show the great potential of the presented models for improving the training of bot detection technology. © 2023 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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OTB-morph: One-Time biometrics via Morphing

arXiv

引用

arXiv 2023年

作者： Ghafourian, Mahdi Fierrez, Julian Vera-Rodriguez, Ruben Morales, Aythami Serna, Ignacio Biometrics and Data Pattern Analytics Lab BiDA Lab Universidad Autonoma de Madrid Madrid28049 Spain

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 systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper is introducing a new idea to exploit as a transformation function for cancelable biometrics aimed at protecting the templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performance. © 2023, CC BY-NC-ND.

关键词： biometrics

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