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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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Progress in pattern Recognition, Image Analysis, Computer Vision, and Applications 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Ruben Vera-Rodriguez Julian Fierrez Aythami Morales

The program was comprised of 6 oral sessions on the following topics: machine learning, computer vision, classification, biometrics and medical applications, and brain signals, and also on: text and character an... 详细信息

ISBN: (数字)9783030134693

ISBN: (纸本)9783030134686

关键词： pattern Recognition Legal Aspects of Computing Software Engineering Information Systems Applications (incl. Internet) Computer Communication Networks Coding and Information Theory

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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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DeepSign: Deep on-line signature verification

arXiv

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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 Universidad Autonoma de Madrid

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 database1, 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. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics

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A Comprehensive Analysis of Factors Impacting Membership Inference

A Comprehensive Analysis of Factors Impacting Membership Inf...

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

作者： Daniel DeAlcala Gonzalo Mancera Aythami Morales Julian Fierrez Ruben Tolosana Javier Ortega-Garcia Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We analyze various factors affecting the proper functioning of MIA and MINT, two research lines aimed at detecting data used for training. The difference between these lines lies in the environmental conditions, while the fundamental bases are similar for both. As evident in the literature, this detection task is far from straightforward and poses an ongoing challenge for the scientific community. Specifically, in this work, we conclude that factors such as the number of times data passes through the original network, the loss function, or dropout significantly impact detection outcomes. Therefore, it is crucial to consider them when developing these methods and during the training of any neural network, both to avoid (MIA) and to enhance (MINT) this detection. We evaluate the AdaFace facial recognition model using five databases with over 22 million images, modifying the different factors under analysis and defining a suitable protocol for their examination. State-of-the-art accuracy reaching up to 87% is achieved, surpassing existing methods.

关键词： Training Analytical models Accuracy Protocols databases Face recognition Biological system modeling

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BeCAPTCHA-Mouse: Synthetic mouse trajectories and improved bot detection

arXiv

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arXiv 2020年

作者： Acien, Alejandro Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

We first study the suitability of behavioral biometrics to distinguish between computers and humans, commonly named as bot detection. We then present BeCAPTCHA-Mouse, a bot detector based on neuromotor modeling of mouse dynamics that enhances traditional CAPTCHA methods. Our proposed bot detector is trained using both human and bot data generated by two new methods developed for generating realistic synthetic mouse trajectories: i) a knowledge-based method based on heuristic functions, and ii) a data-driven method based on Generative Adversarial Networks (GANs) in which a Generator synthesizes human-like trajectories from a Gaussian noise input. Experiments are conducted on a new testbed also introduced here and available in GitHub: BeCAPTCHA-Mouse Benchmark;useful for research in bot detection and other mouse-based HCI applications. Our benchmark data consists of 10,000 mouse trajectories including real data from 58 users and bot data with various levels of realism. Our experiments show that BeCAPTCHA-Mouse is able to detect bot trajectories of high realism with 93% of accuracy in average using only one mouse trajectory. When our approach is fused with state-of-the-art mouse dynamic features, the bot detection accuracy increases relatively by more than 36%, proving that mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems. Copyright © 2020, The Authors. All rights reserved.

关键词： biometrics

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Do You Need More data? The DeepSignDB On-Line Handwritten Signature Biometric database

Do You Need More Data? The DeepSignDB On-Line Handwritten Si...

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International Conference on Document Analysis and Recognition

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

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.

关键词： databases Forgery Writing Training Protocols Task analysis Deep learning

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Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma

arXiv

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arXiv 2023年

作者： DeAlcala, Daniel Serna, Ignacio Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

—The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests. © 2023, CC BY-NC-ND.

关键词： Decision making

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Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma

Measuring Bias in AI Models: An Statistical Approach Introdu...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Daniel DeAlcala Ignacio Serna Aythami Morales Julian Fierrez Javier Ortega-Garcia Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.

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