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

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

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

DeepSign: Deep On-Line Signature Verification

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

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

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

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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

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

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

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

A Comprehensive Study on Face Recognition Biases beyond Demographics

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第1期3卷 16-30页

作者： Terhorst, Philipp Kolf, Jan Niklas Huber, Marco Kirchbuchner, Florian Damer, Naser Moreno, Aythami Morales Fierrez, Julian Kuijper, Arjan Department of Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt64283 Germany Interactive Graphics Systems Group Technical University of Darmstadt Darmstadt64289 Germany Biometrics and Data Pattern Analytics Lab-BiDA Lab Universidad Autonoma de Madrid Madrid28049 Spain

Face recognition (FR) systems have a growing effect on critical decision-making processes. Recent works have shown that FR solutions show strong performance differences based on the user's demographics. However, to enable a trustworthy FR technology, it is essential to know the influence of an extended range of facial attributes on FR beyond demographics. Therefore, in this work, we analyze FR bias over a wide range of attributes. We investigate the influence of 47 attributes on the verification performance of two popular FR models. The experiments were performed on the publicly available MAAD-Face attribute database with over 120M high-quality attribute annotations. To prevent misleading statements about biased performances, we introduced control group-based validity values to decide if unbalanced test data causes the performance differences. The results demonstrate that also many nondemographic attributes strongly affect recognition performance, such as accessories, hairstyles and colors, face shapes, or facial anomalies. The observations of this work show the strong need for further advances in making the FR system more robust, explainable, and fair. Moreover, our findings might help to a better understanding of how FR networks work, enhance the robustness of these networks, and develop more generalized bias-mitigating FR solutions. © 2022 IEEE.

关键词： biometrics

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

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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

DeepFakesON-Phys: Deepfakes detection based on heart rate estimation

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

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

OTB-morph: One-Time biometrics via Morphing

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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

ECG biometric Recognition: Review, System Proposal, and Benchmark Evaluation

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arXiv 2022年

作者： Melzi, Pietro Tolosana, Ruben Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics - BiDA Lab Universidad Autonoma de Madrid Spain

Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubiquity. Also, with the success of Deep Learning technologies, ECG biometric recognition has received increasing interest in recent years. However, it is not easy to evaluate the improvements of novel ECG proposed methods, mainly due to the lack of public data and standard experimental protocols. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. Both verification and identification tasks are investigated, as well as single- and multi-session scenarios. Finally, we also perform single- and multi-lead ECG experiments, considering traditional scenarios using electrodes in the chest and limbs and current user-friendly wearable devices. In addition, we present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database and able to operate successfully across various scenarios and multiple databases. We introduce our proposed feature extractor, trained with multiple sinus-rhythm heartbeats belonging to 55,967 subjects, and provide a general public benchmark evaluation with detailed experimental protocol. We evaluate the system performance over four different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv) CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in verification, and accuracies of 100% and 96.46% in identification, respectively in single- and multi-session analysis. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field. © 2022, CC BY-NC-ND.

关键词： biometrics