Continuous Authentication System Research Articles

Machine learning-based novel continuous authentication system using soft keyboard typing behavior and motion sensor data

Smartphones utilize various authentication methods, including passwords, fingerprints, and face recognition. While this information is quite practical and easy to remember, it introduces several security issues. The primary concerns involve theft, password forgetfulness, or unauthorized password copying. Implementing behavioral biometrics for user authentication adds an extra layer of security. The main contribution of this study is the utilization of soft keyboard typing behavior, a behavioral biometric, for continuous user recognition. To achieve this, the phone's grip style and typing characteristics of users are scrutinized using data collected from motion sensors and the touchscreen panel. Another challenge in mobile device authentication pertains to recognition accuracy and processing time. To expedite and optimize data classification, a hybrid classification structure is suggested. This structure incorporates correlation-based feature selection and a straightforward logistic regression method, offering rapid and highly accurate classification outcomes—a further contribution of this study. Experimental results demonstrate that user identification can be accomplished in as little as 0.03 ms, with a classification accuracy of up to 93%. Continuous authentication systems offer greater security compared to one-time authentication systems. Nevertheless, these systems might not always yield the most precise results. Overcoming this challenge necessitates the development of an efficient software architecture. In line with this, an additional contribution of this study is an explanation of how to construct a continuous authentication system using the developed architecture.

Multi-motion sensor behavior based continuous authentication on smartphones using gated two-tower transformer fusion networks

With continuous authentication, smartphones can constantly calculate authentication scores to verify a user's identity while interacting with the smartphones after logging in. Among various behavioral biometrics based continuous authentication methods, motion dynamics biometrics based methods draw a lot of attention, which relies on potential differences in signals from smartphone built-in motion sensors to provide an implicit and secure authentication. However, existing motion dynamics based methods not only fail to model correlations between motion sensors with multiple channels but also ignore temporal patterns contained in each channel. In this paper, we develop a two-tower neural networks based continuous authentication system TNNAuth based on the built-in multi-motion sensors on smartphones. Specifically, TNNAuth divides the raw multi-motion sensor data into channel and temporal stream data, and proposes a gated two-tower transformer fusion network to capture both time-over-channel and channel-over-time motion patterns. To evaluate TNNAuth, we collect a large-scale multi-motion sensor dataset in the unconstrained real life. Extensive experiments demonstrate that TNNAuth can provide the lowest authentication error rate, with a false-acceptance rate of 8.3% and a false-rejection rate of 9.4%.

PC-based User Continuous Authentication System Using the User's Finger Stroke Characteristics

Biometric technology, which performs continuous authentication based on user behavior, has been developed in various ways depending on the type of device, input device, and sensor. Research on continuous authentication technology in PC-based systems with few sensors installed is based on data from 3physical devices that extract and analyze features from keyboard and mouse input patterns. Among these, previous studies on continuous authentication through keyboard input performed continuous authentication using the key hold delay time that occurs when one key is pressed, the key interval delay time that occurs due to the interaction between fingers, and the key press delay time. did However, the keyboard-based continuous authentication model has limitations in increasing accuracy due to a small number of features. Therefore, in this paper, when a user inputs a sentence using a QWERTY keyboard in a PC system, the function is subdivided by reflecting the characteristics of each finger and used for continuous authentication. The features extracted by reflecting the characteristics of the finger were subdivided into a total of 151 latencies, and SVDD, decision tree, and CNN were used as continuous authentication models. Experimental data was collected through the user's input of randomly displayed sentences, and features were created based on this. User keystroke behavior was used to validate the continuous authentication model. Validation metrics included thresholds for classification accuracy (ACC), ROC curves, false rejection rate (FRR), equal error rate (EER), and false acceptance rate (FAR). As a result of the experiment, it was found that continuous authentication including the user's finger input pattern was superior to the existing method.

Intelligent solution for automatic online exam monitoring

<p><span lang="EN-US">E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording.</span></p>

Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, and OC-SVM classifier. The experimental results show that ADFFDA obtains the best authentication performance w.r.t representative approaches, by achieving a mean equal error rate of 0.01%.

SearchAuth: Neural Architecture Search-based Continuous Authentication Using Auto Augmentation Search

Mobile devices have been playing significant roles in our daily lives, which has made device security and privacy protection extremely important. These mobile devices storing user sensitive and private information, therefore, need rigorous user authentication mechanisms. In this article, we present SearchAuth, a novel continuous authentication system on smartphones exploiting a neural architecture search (NAS) to find an optimal network architecture and an auto augmentation search (AAS) to more effectively train the optimal network along with the best data augmentation policies, by leveraging the accelerometer, gyroscope, and magnetometer on smartphones to capture users’ behavioral patterns. Specifically, SearchAuth consists of three stages, i.e., the offline stage, registration stage, and authentication stage. In the offline stage, we utilize the NAS on sensor data of the accelerometer, gyroscope, and magnetometer to find an optimal network architecture based on the designed search space. With the optimal network architecture, namely, NAS-based model, the AAS automatically optimizes the augmentation of the input data for more effectively training the model that is for feature extraction. In the registration stage, we use the trained NAS-based model to learn and extract deep features from the legitimate user’s data, and train the LOF classifier with 55 features selected by the PCA. In the authentication stage, with the well-trained NAS-based model and LOF classifier, SearchAuth identifies the current user as a legitimate user or an impostor when the user starts operating a smartphone. Based on our dataset, we evaluate the performance of the proposed SearchAuth, and the experimental results demonstrate that SearchAuth surpasses the representative authentication schemes by achieving the best accuracy of 93.95%, F1-score of 94.30%, and EER of 5.30% on the LOF classifier with dataset size of 100.

Deep features fusion for user authentication based on human activity

AbstractThe exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal‐to‐image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach.

Continuous authentication with feature-level fusion of touch gestures and keystroke dynamics to solve security and usability issues

Behavioral Biometrics (BB) Continuous Authentication (CA) systems monitor user behavior and continuously re-authenticate user identity alongside the initial login process. Most studies use single behavioral modality systems to authenticate users. However, the behaviors of genuine users may change, and systems fail when significant changes occur. This results in either usability or security issues. In the literature, the fusion of biometrics is used to solve this problem and achieves improved results. This paper presents our research on the design and evaluation of new approaches to CA using fusion of touch gestures and keystroke dynamics. To collect the biometric data from mobile device users we have developed the BioGames App which follows an innovative approach based on the gamification paradigm. We examine each modality separately and investigate if we can improve the performance results with a feature-level fusion. For this reason, a new appropriate feature set is developed that combines touch gestures and keystroke dynamics. We used the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) and compared their performance. We have shown that feature-level fusion of touch gestures and keystroke dynamics improves the performance of systems and solves security and usability issues. We found that the MLP is superior to LSTM in this context. The MLP achieved Accuracy 98.3% (increased 21.1%), EER 1% (error reduction by 23.7%), TAR 99.4% (increased 46%), TRR 97.4% (increased 10%), FAR 2.6% (reduced by 10.5%), and FRR 0.6% (reduced by 46%).

BLUFADER: Blurred face detection & recognition for privacy-friendly continuous authentication

Authentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers focused on automated de-authentication systems, either as a stand-alone mechanism or as a result of continuous authentication failures. Unfortunately, no single approach offers security, privacy, and usability. Face-recognition methods, for example, may be suitable for security and usability, but they violate user privacy by continuously recording their actions and surroundings.In this work, we propose BLUFADER, a novel continuous authentication system that takes advantage of blurred face detection and recognition to fast, secure, and transparent de-authenticate users, preserving their privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection and recognition continuously. To evaluate BLUFADER’s practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The de-authentication system was trained and evaluated using the former, while the latter was used to appraise the privacy and increase variance at training time. To guarantee the privacy-preserving effectiveness of the selected physical blurring filter, we show that state-of-the-art deblurring models are not able to revert our physical blur. Further, we demonstrate that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Moreover, BLUFADER effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. Last, our continuous authentication face recognition module based on Siamese Neural Network preventively protect users from adversarial attacks, enhancing the overall system security.

A Robust Continuous Authentication System Using Smartphone Sensors and Wasserstein Generative Adversarial Networks

Since the continuous authentication (CA) system based on smartphone sensors has been facing the challenge of the low-data regime under some practical scenarios, which leads to low accuracy of CA, it needs to be solved urgently. To this end, currently, the generative adversarial networks (GAN) provide a powerful method to train the result generative model that could generate very convincing verisimilar data. The framework of the GAN and its variants shed much light on improving the performance of CA. Therefore, in this article, we propose a continuous authentication system on smartphones based on a Wasserstein generative adversarial network (WGAN) for sensor data augmentation, which utilizes accelerometers, gyroscopes, and magnetometers of smartphone sensors to sense phone movements caused by user operation behavior. Specifically, based on sensor data under different user activities, the WGAN is used to create additional data in training data for data augmentation. With the augmented data, we design a convolutional neural network to learn and extract deep features from sensor data, and then use four classifiers of RF, OCSVM, DT, and KNN to train these features. Finally, we train and test on the HMOG dataset, and the results show that the EER of the authentication system is between 3.68% and 6.39% on the sensor data with a time window of 2 s.

AuthConFormer: Sensor-based Continuous Authentication of Smartphone Users Using A Convolutional Transformer

To prevent the leakage of personal information on smartphones, a method that can authenticate the smartphone user’s identity throughout the session is essential. Sensor-based continuous authentication is a promising potential approach that uses behavioral biometrics to authenticate smartphone user’s identity. Recent studies on continuous authentication have achieved encouraging progress. However, they suffer two limitations: 1) weak capability of capturing smartphone user’s behavioral patterns from biometric data sequences with time correlation because these methods don’t consider the long-range dependencies between the behavioral biometric data sequences; 2) poor performance under a high authentication frequency. To solve these issues, we present AuthConFormer, a novel continuous authentication system based on a proposed convolutional transformer, which is suitable for running on the mobile phone CPUs.The proposed convolutional transformer applies an inverted residual block instead of linear projection in the pure transformer to perform the query, key and value embeddings, respectively. Compared with the pure transformer, the proposed convolutional transformer requires less computation, and can make full use of the contextual information of 2D feature maps. We perform several experiments to evaluate the performance and effectiveness of the AuthConFormer on three public datasets. Experimental results show that the proposed AuthConFormer can achieve the mean values of 1.06% EER, 1.25% EER and 1.19% EER on three public datasets with a high authentication frequency (one authentication every 0.6s), respectively. Besides, the convolutional transformer model pre-trained on the HMOG dataset exhibits good cross-domain generalization capability on the BrainRun dataset.

A Supervised ML Biometric Continuous Authentication System for Industry 4.0

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled.

GANTouch: An Attack-Resilient Framework for Touch-Based Continuous Authentication System

Previous studies have shown that commonly studied (vanilla) implementations of touch-based continuous authentication systems (V-TCAS) are susceptible to active adversarial attempts. This study presents a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under three active adversarial environments viz. Zero-effort, Population, and Random-vector. The Zero-effort environment was implemented in two variations viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first involved a Zero-effort attack from the same dataset, while the second used three different datasets. G-TCAS showed more resilience than V-TCAS under the Population and Random-vector, the more damaging adversarial scenarios than the Zero-effort. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (27.5% and 21.5%) than for G-TCAS (14% and 12.5%) for Population and Random-vector attacks, respectively. Moreover, we performed a fairness analysis of TCAS for different genders and found TCAS to be fair across genders. The findings suggest that we should evaluate TCAS under active adversarial environments and affirm the usefulness of GANs in the TCAS pipeline.

Combating False Data Injection Attacks on Human-Centric Sensing Applications

The recent prevalence of machine learning-based techniques and smart device embedded sensors has enabled widespread human-centric sensing applications. However, these applications are vulnerable to false data injection attacks (FDIA) that alter a portion of the victim's sensory signal with forged data comprising a targeted trait. Such a mixture of forged and valid signals successfully deceives the continuous authentication system (CAS) to accept it as an authentic signal. Simultaneously, introducing a targeted trait in the signal misleads human-centric applications to generate specific targeted inference; that may cause adverse outcomes. This paper evaluates the FDIA's deception efficacy on sensor-based authentication and human-centric sensing applications simultaneously using two modalities - accelerometer, blood volume pulse signals. We identify variations of the FDIA such as different forged signal ratios, smoothed and non-smoothed attack samples. Notably, we present a novel attack detection framework named Siamese-MIL that leverages the Siamese neural networks' generalizable discriminative capability and multiple instance learning paradigms through a unique sensor data representation. Our exhaustive evaluation demonstrates Siamese-MIL's real-time execution capability and high efficacy in different attack variations, sensors, and applications.

Data-Driven Behavioural Biometrics for Continuous and Adaptive User Verification Using Smartphone and Smartwatch

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.

BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures

PurposeThis research aims to build a system that will continuously. This paper is an extended version of SECPRE 2021 paper and presents a research on the development and validation of a behavioral biometrics continuous authentication (BBCA) system that is based on users keystroke dynamics and touch gestures on mobile devices. This paper aims to build a system that will continuously authenticate the user of a smartphone.Design/methodology/approachSession authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, BBCA technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), which is based on the user’s keystroke dynamics and touch gestures, using a multi-layer perceptron (MLP). Also, this paper introduces a new BB collection tool and proposes a methodology for the selection of an appropriate set of BB.FindingsThe system achieved the best results for keystroke dynamics which are 97.18% accuracy, 0.02% equal error rate, 97.2% true acceptance rate and 0.02% false acceptance rate.Originality/valueThis paper develops a new BB collection tool, named BioPrivacy, by which behavioral data of users on mobile devices can be collected. This paper proposes a methodology for the selection of an appropriate set of BB. This paper presents the development of a BBCA system based on MLP.

Improving Mobile Device Security by Embodying and Co-adapting a Behavioral Biometric Interface

At present, interfaces between users and smart devices such as smart phones rely primarily on passwords. This has allowed for the intrusion and perturbation of the interface between the user and the device and has compromised security. Recently, Frank et al. have suggested that security could be improved by having an interface with biometric features of finger swiping. This approach has been termed touchalytics, in maintaining cybersecurity. The number of features of finger swiping have been large (32) and have been made available as a public database, which we utilize in our study. However, it has not been shown which of these features uniquely identify a particular user. In this paper, we study whether a subset of features that embody human cognitive motor features can be used to identify a particular user. We consider how the security might be made more efficient embodying Principal Component Analysis (PCA) into the interface, which has the potential of reducing the features utilized in the identification of intruders. We compare the accuracy and performance of the reduced feature space to that of having all the features. Embodying a robust continuous authentication system will give users an extra layer of security and an increased sense of peace of mind if their devices are lost or stolen. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. If continuous authentication models become successful and easily implementable, embodiment and co-adaptation of user authentication would inhibit the growing problem of mobile device theft.

AI-based user authentication reinforcement by continuous extraction of behavioral interaction features

In this work, we conduct an experiment to analyze the feasibility of a continuous authentication method based on the monitorization of the users’ activity to verify their identities through specific user profiles modeled via Artificial Intelligence techniques. In order to conduct the experiment, a custom application was developed to gather user records in a guided scenario where some predefined actions must be completed. This dataset has been anonymized and will be available to the community. Additionally, a public dataset was also used for benchmarking purposes so that our techniques could be validated in a non-guided scenario. Such data were processed to extract a number of key features that could be used to train three different Artificial Intelligence techniques: Support Vector Machines, Multi-Layer Perceptrons, and a Deep Learning approach. These techniques demonstrated to perform well in both scenarios, being able to authenticate users in an effective manner. Finally, a rejection test was conducted, and a continuous authentication system was proposed and tested using weighted sliding windows, so that an impostor could be detected in a real environment when a legitimate user session is hijacked.

Key factors driving the adoption of behavioral biometrics and continuous authentication technology: an empirical research

PurposeFor the success of future investments in the implementation of continuous authentication systems, we should explore the key factors that influence technology adoption. The authors investigate the effect of various factors of behavioral intention through the new incorporation of a modified technology acceptance model (TAM) and diffusion of innovation theory (DOI). Also, the authors have created a new theoretical framework with constructs such as security and privacy risks (SPR), biometrics privacy concerns (BPC) and perceived risk of using the technology (PROU). In this paper, the authors conducted a structural equation modeling empirical research. This research is designed in such a way to respond to the trade-off between users’ concern for the protection of their biometrics privacy and their protection from risks.Design/methodology/approachThe authors provide an extensive conceptual framework for both existing models (TAM and DOI) and the new constructs that the authors have added to the model. In addition, this research explores external factors, such as trust in technology (TT) and innovativeness (Innov). In addition, the authors have introduced significant constructs, to overcome the limitations of the TAM and to adapt it to the needs of the present research. The new theoretical framework the authors introduce in the present research concerns the constructs SPR, BPC and PROU.FindingsThe authors found that the main facilitators of behavioral intention to adopt the technology (BI) are TT, followed by compatibility (COMP), perceived usefulness (PU) and Innov. This research also shows that individuals are less interested in the ease of use of the technology and are willing to sacrifice it to achieve greater security. COMP and Innov also play a significant role. Individuals who believe that the usage of the behavioral biometrics continuous authentication (BBCA) technology would fit into their lifestyle and would like to experiment with new technologies have a positive intention to adopt the BBCA technology. The new constructs the authors have added are SPR, BPC and PROU. The authors’ results support the hypotheses that SPR is a facilitator to PU and PU acts as a facilitator to BI. Consequently, the hypothesis that individuals do not feel adequately protected by classical methods will consider the usefulness of the BBCA as a technology for their extra protection against risks is confirmed by the model. Also, with the constructs BPC and PROU, the authors examined if individuals’ concerns regarding their biometrics privacy act as inhibitors in the BI. The authors concluded that individuals consider that the benefits of using BBCA technology are much more important than the risks for their biometrics privacy since the hypothesis that the major inhibitor of BI is PROU is not supported by the model.Originality/valueTo the best of the authors’ knowledge, this research is among the first in the field that examines the factors that influence the individuals’ decision to adopt BBCA technology.

Combining user behavioural information at the feature level to enhance continuous authentication systems

The scientific and business communities are proposing new authentication methods more robust than traditional solutions relying on a single security point such as passwords (i.e. “something you know”). User and Entity Behavior Analysis (UEBA) has postulated as an excellent solution to improve authentication systems by performing continuous authentication to extend the authentication process over time. UEBA is based on detecting anomalies in the intrinsic behaviour of each user or entity (i.e. it is based on “something you are/do”). This paper presents a method for performing continuous authentication using UEBA techniques that allows combining information from multiple sources at the feature level. This combination is achieved through a novel Symbolic Aggregate approximation (SAX) using Random Trees Embeddings for each information source, producing a sequence of symbols. Then, these sequences of symbols are combined into a single sequence using temporal information. The resulting sequence of symbols feeds a density-based clustering model that uses a distance based on DNA sequence alignment techniques to extract behavioural cores. Finally, new samples are compared against these cores to detect anomalies using a risk model that evaluates if a behaviour is anomalous (suspected user impersonation). The model has been extensively tested and evaluated against well-known state-of-the-art datasets.