检索结果-内蒙古大学图书馆

A survey of privacy vulnerabilities of mobile device sensors

学校读者我要写书评

暂无评论

arXiv 2021年

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

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field. © 2021, CC BY-NC-ND.

关键词： Surveys

Mobile Behavioral biometrics for Passive Authentication

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami Acien, Alejandro Le Lan, Gaël Biometrics And Data Pattern Analytics Lab Universidad Autonoma De Madrid Spain Orange Labs Cesson-Sevigne France

Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval. © 2022, CC BY-NC-ND.

关键词： Authentication

Assessing the Quality of Swipe Interactions for Mobile Biometric Systems

学校读者我要写书评

暂无评论

Assessing the Quality of Swipe Interactions for Mobile Biome...

IEEE International Joint Conference on biometrics (IJCB)

作者： Marco Santopietro Ruben Vera-Rodriguez Richard Guest Aythami Morales Alejandro Acien University of Kent Canterbury UK Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid Spain

The following topics are dealt with: face recognition; feature extraction; biometrics (access control); convolutional neural nets; learning (artificial intelligence); deep learning (artificial intelligence); iris reco... 详细信息

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

关键词： Feature extraction biometrics (access control) Face recognition Authentication Statistics Sociology Task analysis

Optimizing Key-Selection for Face-Based One-Time biometrics via Morphing

学校读者我要写书评

暂无评论

Optimizing Key-Selection for Face-Based One-Time Biometrics ...

IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Dailé Osorio-Roig Mahdi Ghafourian Christian Rathgeb Ruben Vera-Rodriguez Christoph Busch Julian Fierrez Biometrics and Internet Security Research Group Hochschule Darmstadt Germany Biometrics and Data Pattern Analytics (BiDA) Lab Universidad Autonoma de Madrid (UAM) Spain

Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed to provide protection of stored biometric reference data, i.e. templates. In the literature, privacy-enhancing facial recognition approaches have focused solely on conventional security threats at the template level, ignoring the growing concern related to adversarial attacks. Up to now, few works have provided mechanisms to protect face recognition against adversarial attacks while maintaining high security at the template level. In this paper, we propose different key selection strategies to improve the security of a competitive cancelable scheme operating at the signal level. Experimental results show that certain strategies based on signal-level key selection can lead to complete blocking of the adversarial attack based on an iterative optimization for the most secure threshold, while for the most practical threshold, the attack success chance can be decreased to approximately 5.0%.

关键词：

Mobile Keystroke biometrics Using Transformers

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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.

关键词：