Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic

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.Comment: arXiv admin note: text overlap with arXiv:2004.0362

SetMargin loss applied to deep keystroke biometrics with circle packing interpretation

This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML). DML maps input data into a learned representation space that reveals a “semantic” structure based on distances. In this work, we propose a novel DML method specifically designed to address the challenges associated to free-text keystroke identification where the classes used in learning and inference are disjoint. The proposed SetMargin Loss (SM-L) extends traditional DML approaches with a learning process guided by pairs of sets instead of pairs of samples, as done traditionally. The proposed learning strategy allows to enlarge inter-class distances while maintaining the intra-class structure of keystroke dynamics. We analyze the resulting representation space using the mathematical problem known as Circle Packing, which provides neighbourhood structures with a theoretical maximum inter-class distance. We finally prove experimentally the effectiveness of the proposed approach on a challenging task: keystroke biometric identification over a large set of 78,000 subjects. Our method achieves state-of-the-art accuracy on a comparison performed with the best existing approachesThis work has been supported by projects: PRIMA ( MSCA-ITN- 2019-860315 ), TRESPASS-ETN (MSCA-ITN-2019-860813), BIBECA (RTI2018-101248-B-I00 MINECO), edBB (UAM), and Instituto de In- genieria del Conocimiento (IIC). A. Acien is supported by a FPI fel- lowship from the Spanish MINEC

Active detection of age groups based on touch interaction

This paper studies user classification into children and adults according to their interaction with touchscreen devices. We analyse the performance of two sets of features derived from the Sigma-Lognormal theory of rapid human movements and global characterization of touchscreen interaction. We propose an active detection approach aimed to continuously monitorize the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. We have used Support Vector Machines algorithm to classify the resulting features into age groups. The sets of features are fused at score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotorinspired features to classify age groups according to the interaction with touch devices. In active detection setup, our method is able to identify a child using only 4 gestures in averageThis work was funded by the project CogniMetrics (TEC2015-70627-R) and Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017

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

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.Comment: Paper accepted in IEEE Computer Society Workshop on Biometrics (CVPRw) 202

Assessing the Quality of Swipe Interactions for Mobile Biometric Systems

The following topics are dealt with: face recognition; feature extraction; biometrics (access control); convolutional neural nets; learning (artificial intelligence); deep learning (artificial intelligence); iris recognition; image classification; image matching; image recognition