A survey on touch dynamics authentication in mobile devices

© 2016 Elsevier Ltd. All rights reserved. There have been research activities in the area of keystroke dynamics biometrics on physical keyboards (desktop computers or conventional mobile phones) undertaken in the past three decades. However, in terms of touch dynamics biometrics on virtual keyboards (modern touchscreen mobile devices), there has been little published work. Particularly, there is a lack of an extensive survey and evaluation of the methodologies adopted in the area. Owing to the widespread use of touchscreen mobile devices, it is necessary for us to examine the techniques and their effectiveness in the domain of touch dynamics biometrics. The aim of this paper is to provide some insights and comparative analysis of the current state of the art in the topic area, including data acquisition protocols, feature data representations, decision making techniques, as well as experimental settings and evaluations. With such a survey, we can gain a better understanding of the current state of the art, thus identifying challenging issues and knowledge gaps for further research

Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used). © 2019, The Author(s)

A robust abnormal behavior detection method using convolutional neural network

A behavior is considered abnormal when it is seen as unusual under certain contexts. The definition for abnormal behavior varies depending on situations. For example, people running in a field is considered normal but is deemed abnormal if it takes place in a mall. Similarly, loitering in the alleys, fighting or pushing each other in public areas are considered abnormal under specific circumstances. Abnormal behavior detection is crucial due to the increasing crime rate in the society. If an abnormal behavior can be detected earlier, tragedies can be avoided. In recent years, deep learning has been widely applied in the computer vision field and has acquired great success for human detection. In particular, Convolutional Neural Network (CNN) has shown to have achieved state-of-the-art performance in human detection. In this paper, a CNN-based abnormal behavior detection method is presented. The proposed approach automatically learns the most discriminative characteristics pertaining to human behavior from a large pool of videos containing normal and abnormal behaviors. Since the interpretation for abnormal behavior varies across contexts, extensive experiments have been carried out to assess various conditions and scopes including crowd and single person behavior detection and recognition. The proposed method represents an end-to-end solution to deal with abnormal behavior under different conditions including variations in background, number of subjects (individual, two persons or crowd), and a range of diverse unusual human activities. Experiments on five benchmark datasets validate the performance of the proposed approach

Sensing Your Touch: Strengthen User Authentication via Touch Dynamic Biometrics

© 2019 IEEE. Mobile devices are increasingly used to store private and sensitive data, and this has led to an increased demand for more secure and usable authentication services. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To make the authentication service more secure and reliable for mobile device users, this paper describes our efforts in investigating the benefits of integrating a touch dynamics authentication method into a PIN-based authentication method. It describes the design, implementation and evaluation of this method. Experimental results show that this approach can significantly reduce the success rate of impersonation attempts; in the case of a 4-digit PIN, the success rate is reduced from 100% (if only the PIN is used) to 9.9% (if both the PIN and the touch dynamics are used)

A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads [version 2; peer review: 2 approved]

Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions

FOXM1 Induces a Global Methylation Signature That Mimics the Cancer Epigenome in Head and Neck Squamous Cell Carcinoma

The oncogene FOXM1 has been implicated in all major types of human cancer. We recently showed that aberrant FOXM1 expression causes stem cell compartment expansion resulting in the initiation of hyperplasia. We have previously shown that FOXM1 regulates HELLS, a SNF2/helicase involved in DNA methylation, implicating FOXM1 in epigenetic regulation. Here, we have demonstrated using primary normal human oral keratinocytes (NOK) that upregulation of FOXM1 suppressed the tumour suppressor gene p16INK4A (CDKN2A) through promoter hypermethylation. Knockdown of HELLS using siRNA re-activated the mRNA expression of p16INK4A and concomitant downregulation of two DNA methyltransferases DNMT1 and DNMT3B. The dose-dependent upregulation of endogenous FOXM1 (isoform B) expression during tumour progression across a panel of normal primary NOK strains (n = 8), dysplasias (n = 5) and head and neck squamous cell carcinoma (HNSCC) cell lines (n = 11) correlated positively with endogenous expressions of HELLS, BMI1, DNMT1 and DNMT3B and negatively with p16INK4A and involucrin. Bisulfite modification and methylation-specific promoter analysis using absolute quantitative PCR (MS-qPCR) showed that upregulation of FOXM1 significantly induced p16INK4A promoter hypermethylation (10-fold, P<0.05) in primary NOK cells. Using a non-bias genome-wide promoter methylation microarray profiling method, we revealed that aberrant FOXM1 expression in primary NOK induced a global hypomethylation pattern similar to that found in an HNSCC (SCC15) cell line. Following validation experiments using absolute qPCR, we have identified a set of differentially methylated genes, found to be inversely correlated with in vivo mRNA expression levels of clinical HNSCC tumour biopsy samples. This study provided the first evidence, using primary normal human cells and tumour tissues, that aberrant upregulation of FOXM1 orchestrated a DNA methylation signature that mimics the cancer methylome landscape, from which we have identified a unique FOXM1-induced epigenetic signature which may have clinical translational potentials as biomarkers for early cancer screening, diagnostic and/or therapeutic interventions

Validation of diffusion tensor MRI measurements of cardiac microstructure with structure tensor synchrotron radiation imaging.

Background Diffusion tensor imaging (DTI) is widely used to assess tissue microstructure non-invasively. Cardiac DTI enables inference of cell and sheetlet orientations, which are altered under pathological conditions. However, DTI is affected by many factors, therefore robust validation is critical. Existing histological validation is intrinsically flawed, since it requires further tissue processing leading to sample distortion, is routinely limited in field-of-view and requires reconstruction of three-dimensional volumes from two-dimensional images. In contrast, synchrotron radiation imaging (SRI) data enables imaging of the heart in 3D without further preparation following DTI. The objective of the study was to validate DTI measurements based on structure tensor analysis of SRI data. Methods One isolated, fixed rat heart was imaged ex vivo with DTI and X-ray phase contrast SRI, and reconstructed at 100 μm and 3.6 μm isotropic resolution respectively. Structure tensors were determined from the SRI data and registered to the DTI data. Results Excellent agreement in helix angles (HA) and transverse angles (TA) was observed between the DTI and structure tensor synchrotron radiation imaging (STSRI) data, where HADTI-STSRI = −1.4° ± 23.2° and TADTI-STSRI = −1.4° ± 35.0° (mean ± 1.96 standard deviation across all voxels in the left ventricle). STSRI confirmed that the primary eigenvector of the diffusion tensor corresponds with the cardiomyocyte long-axis across the whole myocardium. Conclusions We have used STSRI as a novel and high-resolution gold standard for the validation of DTI, allowing like-with-like comparison of three-dimensional tissue structures in the same intact heart free of distortion. This represents a critical step forward in independently verifying the structural basis and informing the interpretation of cardiac DTI data, thereby supporting the further development and adoption of DTI in structure-based electro-mechanical modelling and routine clinical applications

Success Rate of Split-Thickness Skin Grafting of Chronic Venous Leg Ulcers Depends on the Presence of Pseudomonas aeruginosa: A Retrospective Study

The last years of research have proposed that bacteria might be involved in and contribute to the lack of healing of chronic wounds. Especially it seems that Pseudomonas aeruginosa play a crucial role in the healing. At Copenhagen Wound Healing Centre it was for many years clinical suspected that once chronic venous leg ulcers were colonized (weeks or months preoperatively) by P. aeruginosa, the success rate of skin grafting deteriorated despite aggressive treatment. To investigate this, a retrospective study was performed on the clinical outcome of 82 consecutive patients with chronic venous leg ulcers on 91 extremities, from the 1st of March 2005 until the 31st of August 2006. This was achieved by analysing the microbiology, demographic data, smoking and drinking habits, diabetes, renal impairment, co-morbidities, approximated size and age of the wounds, immunosuppressive treatment and complicating factors on the clinical outcome of each patient. The results were evaluated using a Student T-test for continuous parameters, chi-square test for categorical parameters and a logistic regression analysis to predict healing after 12 weeks. The analysis revealed that only 33,3% of ulcers with P. aeruginosa, isolated at least once from 12 weeks prior, to or during surgery, were healed (98% or more) by week 12 follow-up, while 73,1% of ulcers without P. aeruginosa were so by the same time (p = 0,001). Smoking also significantly suppressed the outcome at the 12-week follow-up. Subsequently, a logistic regression analysis was carried out leaving P. aeruginosa as the only predictor left in the model (p = 0,001). This study supports our hypothesis that P. aeruginosa in chronic venous leg ulcers, despite treatment, has considerable impact on partial take or rejection of split-thickness skin grafts

MicroRNAs in Renal Cell Carcinoma: Diagnostic Implications of Serum miR-1233 Levels

BACKGROUND: MicroRNA expression is altered in cancer cells, and microRNAs could serve as diagnostic/prognostic biomarker for cancer patients. Our study was designed to analyze circulating serum microRNAs in patients with renal cell carcinoma (RCC). METHODOLOGY/PRINCIPAL FINDINGS: We first explored microrna expression profiles in tissue and serum using taqman low density arrays in each six malignant and benign samples: Although 109 microRNAs were circulating at higher levels in cancer patients' serum, we identified only 36 microRNAs with up-regulation in RCC tissue and serum of RCC patients. Seven candidate microRNAs were selected for verification based on the finding of up-regulation in serum and tissue of RCC patients: miR-7-1*, miR-93, miR-106b*, miR-210, miR-320b, miR-1233 and miR-1290 levels in serum of healthy controls (n = 30) and RCC (n = 33) patients were determined using quantitative real-time PCR (TaqMan MicroRNA Assays). miR-1233 was increased in RCC patients, and thus validated in a multicentre cohort of 84 RCC patients and 93 healthy controls using quantitative real-time PCR (sensitivity 77.4%, specificity 37.6%, AUC 0.588). We also studied 13 samples of patients with angiomyolipoma or oncocytoma, whose serum miR-1233 levels were similar to RCC patients. Circulating microRNAs were not correlated with clinical-pathological parameters. CONCLUSIONS/SIGNIFICANCE: MicroRNA levels are distinctly increased in cancer patients, although only a small subset of circulating microRNAs has a tumor-specific origin. We identify circulating miR-1233 as a potential biomarker for RCC patients. Larger-scaled studies are warranted to fully explore the role of circulating microRNAs in RCC

Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R2 = 0.70) and maximum rates of photosynthesis (R2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth