Designing reliable user authentication on mobile phones is becoming an increasingly important task to protect users' private information and data. Since biometric approaches can provide many advantages over the traditional authentication methods, they have become a significant topic for both academia and industry. The major goal of biometric user authentication is to authenticate legitimate users and identify impostors based on physiological and behavioral characteristics. In this paper, we survey the development of existing biometric authentication techniques on mobile phones, particularly on touch-enabled devices, with reference to 11 biometric approaches (five physiological and six behavioral). We present a taxonomy of existing efforts regarding biometric authentication on mobile phones and analyze their feasibility of deployment on touch-enabled mobile phones. In addition, we systematically characterize a generic biometric authentication system with eight potential attack points and survey practical attacks and potential countermeasures on mobile phones. Moreover, we propose a framework for establishing a reliable authentication mechanism through implementing a multimodal biometric user authentication in an appropriate way. Experimental results are presented to validate this framework using touch dynamics, and the results show that multimodal biometrics can be deployed on touch-enabled phones to significantly reduce the false rates of a single biometric system. Finally, we identify challenges and open problems in this area and suggest that touch dynamics will become a mainstream aspect in designing future user authentication on mobile phones.

Surveying the Development of Biometric User Authentication on Mobile Phones

In this paper we discuss the social and ethical issues that arise as a result of digitization based on six dominant technologies: Internet of Things, robotics, biometrics, persuasive technology, virtual & augmented reality, and digital platforms. We highlight the many developments in the digitizing society that appear to be at odds with six recurring themes revealing from our analysis of the scientific literature on the dominant technologies: privacy, autonomy, security, human dignity, justice, and balance of power. This study shows that the new wave of digitization is putting pressure on these public values. In order to effectively shape the digital society in a socially and ethically responsible way, stakeholders need to have a clear understanding of what such issues might be. Supervision has been developed the most in the areas of privacy and data protection. For other ethical issues concerning digitization such as discrimination, autonomy, human dignity and unequal balance of power, the supervision is not as well organized.

/pdf/societal-and-ethical-issues-of-digitization-nbiqgnwlcr.pdf

Societal and ethical issues of digitization

In this study, our purpose was to improve the performance of our mass detection system by using a new dual system approach which combines a computer-added detection (CAD) system optimized with "average" masses with another CAD system optimized with "subtle" masses. The two single CAD systems have similar image processing steps, which include prescreening, object segmentation, morphological and texture feature extraction, and false positive (FP) reduction by rule-based and linear discriminant analysis (LDA) classifiers. A feed-forward backpropagation artificial neural network was trained to merge the scores from the LDA classifiers in the two single CAD systems and differentiate true masses from normal tissue. For an unknown test mammogram, the two single CAD systems are applied to the image in parallel to detect suspicious objects. A total of three data sets were used for training and testing the systems. The first data set of 230 current mammograms, referred to as the average mass set, was collected from 115 patients. We also collected 264 mammograms, referred to as the subtle mass set, which were one to two years prior to the current exam from these patients. Both the average and the subtle mass sets were partitioned into two independent data sets in a cross validation training and testing scheme. A third data set containing 65 cases with 260 normal mammograms was used to estimate the FP marker rates during testing. When the single CAD system trained on the average mass set was applied to the test set with average masses, the FP marker rates were 2.2, 1.8, and 1.5 per image at the case-based sensitivities of 90%, 85%, and 80%, respectively. With the dual CAD system, the FP marker rates were reduced to 1.2, 0.9, and 0.7 per image, respectively, at the same case-based sensitivities. Statistically significant (p < 0.05) improvements on the free response receiver operating characteristic curves were observed when the dual system and the single system were compared using the test sets with either average masses or subtle masses.

Dual system approach to computerâ aided detection of breast masses on mammograms

Improving the robustness of DCT-based image watermarking against JPEG compression

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.

/pdf/security-and-accuracy-of-fingerprint-based-biometrics-a-m2xy84899b.pdf

Security and Accuracy of Fingerprint-Based Biometrics: A Review

Visual secret sharing scheme (VSS) based on the concept of random grid (RG) that suffers from low visual quality of the decrypted secret. In this work a (2, n) RG based VSS has been proposed to improve the contrast. The proposed scheme supports the traditional stacking decryption method. To improve the visual quality further a decryption algorithm is proposed. The experimental result and comparison of visual quality with related methods has shown that the proposed schemes perform well.

Improving the Visual Quality of (2, n ) Random Grid Based Visual Secret Sharing Schemes

In this paper we focus on constructing k-Nearest Neighborhood Structure(k - NNS) for minutiae points in a fingerprint image. For each minutiae point in a fingerprint, a k - NNS is constructed taking the local and global features of minutiae points. This structure is quantized and mapped onto a 2D array to generate a fixed length 1D bit-string. Further this bit string is applied with a DFT to generate a complex vector. Finally the complex vector is multiplied by a user specific random matrix to generate the cancelable template. We tested our proposed method on database FV C2002 and experimental results depicts the validity of the proposed method in terms of requirements of cancelable biometrics namely diversity, accuracy, irreversibility and revocability.

k-Nearest Neighborhood Structure (k-NNS) based alignment-free method for fingerprint template protection

Time series data is a sequence of values recorded systematically over a period which are mostly used for prediction, clustering, and analysis. The two essential features of a time series data are trend and seasonality. Preprocessing of the time series data is necessary for performing prediction tasks. In most of the cases, the trend and the seasonality are removed before applying the regression algorithms. The accuracy of such algorithms depends upon the functions used for the removal of trend and seasonality. Clustering of an unlabeled time series data with the presence of trend and seasonality is challenging. In this paper, we propose a probabilistic representational learning method for grouping the time series data. We introduce five terminologies in our method of clustering namely the trendlets, uplets, downlets, equalets and trendlet string. These elements are the representational building blocks of our proposed method. Experiments on the proposed algorithm are performed with the renewable energy data on the electricity supply system of continental Europe which includes the demand and inflow of renewable energy for the term 2012 to 2014 and UCR-2018 time series archive containing 128 datasets. We compared our proposed representational method with various clustering algorithms using the silhouette score. Mini-batch k-means and agglomerative hierarchical clustering algorithms show better performance in terms of quality, logical accordance with data and time taken for clustering.

Trendlets: A novel probabilistic representational structures for clustering the time series data

Time series sequences include a series of values recorded in specific time intervals which expose the functionality and behavior of data elements in a respective domain. Grouping of these elements based on the trend of their attribute values is challenging in an unsupervised learning environment. New representational structures are needed to explore the trend among unlabeled time series elements through clustering. We propose a fuzzy representational method and structure named fuzzylets, which can be used for unsupervised clustering of time series elements based on the trend of respective series. Fuzzylets provide a flexibility for various time series elements to discover their similarity based on the fuzzy membership of the trend existing among them. The fuzzylets are clustered using traditional hierarchical clustering and compared with other methods using the silhouette scores obtained from the clustering results. We performed an experimental analysis with fuzzylets on the electric energy dataset which contains the inflow and outflow of renewable energy in continental Europe for the tenure from 2012 to 2014 and UCR-2018 time series database containing 128 datasets. We compared fuzzylet based classification with six traditional methods. Fuzzylet based classification algorithms show better accuracy than others which reveals the importance of this novel time series primitives in time series feature learning.

Fuzzy representational structures for trend based analysis of time series clustering and classification

Digital Watermarking Techniques for Images: Survey

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