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Mobile Keystroke biometrics Using Transformers

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arXiv 2022年

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

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 knowledge, 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. © 2022, CC BY-NC-ND.

关键词： biometrics

BioTouchPass2: Touchscreen password biometrics using time-aligned recurrent neural networks

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arXiv 2020年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Ortega-Garcia, Javier Biometrics and Data Pattern Analytics BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain

Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)1. This approach combines the potential of DTW and RNNs to train more robust systems against attacks. A complete analysis of the proposed approach is carried out using both MobileTouchDB2 and e-BioDigitDB3 databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

BioTouchPass: Handwritten Passwords for Touchscreen biometrics

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arXiv 2022年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Fierrez, Julian Biometrics and Data Pattern Analytics - BiDA Lab Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain

This work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and OneTime Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the robustness when increasing the length of the password and the number of enrolment samples. The new e-BioDigit database, which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. This database is used in the experiments reported in this work and it is available together with benchmark results in GitHub1. Finally, we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with Equal Error Rates (EERs) ca. 4.0% when the attacker knows the password. These results encourage the deployment of our proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under the same impostor scenario. © 2022, CC BY-NC-ND.

关键词： Authentication

Exploring Transformers for Behavioural biometrics: A Case Study in Gait Recognition

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arXiv 2022年

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

biometrics on mobile devices has attracted a lot of attention in recent years as it is considered a user-friendly authentication method. This interest has also been motivated by the success of Deep Learning (DL). Architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have established to be convenient for the task, improving the performance and robustness in comparison to traditional machine learning techniques. However, some aspects must still be revisited and improved. To the best of our knowledge, this is the first article that intends to explore and propose novel gait biometric recognition systems based on Transformers, which currently obtain state-of-the-art performance in many applications. Several state-of-the-art architectures (Vanilla, Informer, Autoformer, Block-Recurrent Transformer, and THAT) are considered in the experimental framework. In addition, new configurations of the Transformers are proposed to further increase the performance. Experiments are carried out using the two popular public databases whuGAIT and OU-ISIR. The results achieved prove the high ability of the proposed Transformer, outperforming state-of-the-art CNN and RNN architectures. © 2022, CC BY-NC-ND.

关键词： biometrics

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

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

IEEE Computer Society Conference on Computer Vision and pattern Recognition Workshops (CVPRW)

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

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.

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GaitPrivacyON: Privacy-Preserving Mobile Gait biometrics using Unsupervised Learning

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arXiv 2021年

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

Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) two convolutional Autoencoders with shared weights that transform attributes of the biometric raw data, such as the gender or the activity being performed, into a new privacy-preserving representation;and ii) a mobile gait verification system based on the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a Siamese architecture. The main advantage of GaitPrivacyON is that the first module (convolutional Autoencoders) is trained in an unsupervised way, without specifying the sensitive attributes of the subject to protect. Two experimental studies have been examinated: i) MotionSense and MobiAct databases;and ii) OU-ISIR database. The experimental results achieved suggest the potential of GaitPrivacyON to significantly improve the privacy of the subject while keeping user authentication results higher than 96.6% Area Under the Curve (AUC). To the best of our knowledge, this is the first mobile gait verification approach that considers privacy-preserving methods trained in an unsupervised way. © 2021, CC BY-NC-ND.

关键词： biometrics

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

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arXiv 2022年

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

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 the 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, in few-shot learning scenarios it represents an important challenge. Furthermore, these results show the great potential of the presented models. © 2022, CC BY-NC-ND.

关键词： biometrics

Keystroke biometrics in response to fake news propagation in a global pandemic

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arXiv 2020年

作者： Morales, Aythami Acien, Alejandro Fierrez, Julian Monaco, John V. Tolosana, Ruben Vera-Rodriguez, Ruben Ortega-Garcia, Javier Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Naval Postgraduate School MonterreyCA United States

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics

Synthetic data for the Mitigation of Demographic Biases in Face Recognition

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Synthetic Data for the Mitigation of Demographic Biases in F...

IEEE International Joint Conference on biometrics (IJCB)

作者： Pietro Melzi Christian Rathgeb Ruben Tolosana Ruben Vera-Rodriguez Aythami Morales Dominik Lawatsch Florian Domin Maxim Schaubert Biometrics and Data Pattern Analytics Laboratory Universidad Autonoma de Madrid Spain secunet Security Networks AG Essen Germany Hochschule Darmstadt Germany

This study investigates the possibility of mitigating the demographic biases that affect face recognition technologies through the use of synthetic data. Demographic biases have the potential to impact individuals from specific demographic groups, and can be identified by observing disparate performance of face recognition systems across demographic groups. They primarily arise from the unequal representations of demographic groups in the training data. In recent times, synthetic data have emerged as a solution to some problems that affect face recognition systems. In particular, during the generation process it is possible to specify the desired demographic and facial attributes of images, in order to control the demographic distribution of the synthesized dataset, and fairly represent the different demographic groups. We propose to fine-tune with synthetic data existing face recognition systems that present some demographic biases. We use synthetic datasets generated with GANDiffFace, a novel framework able to synthesize datasets for face recognition with controllable demographic distribution and realistic intra-class variations. We consider multiple datasets representing different demographic groups for training and evaluation. Also, we fine-tune different face recognition systems, and evaluate their demographic fairness with different metrics. Our results support the proposed approach and the use of synthetic data to mitigate demographic biases in face recognition.

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