A Large-scale Empirical Analysis of Browser Fingerprints Properties for Web Authentication

Modern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security by augmenting web authentication mechanisms. In this article, we investigate the adequacy of browser fingerprints for web authentication. We make the link between the digital fingerprints that distinguish browsers, and the biological fingerprints that distinguish Humans, to evaluate browser fingerprints according to properties inspired by biometric authentication factors. These properties include their distinctiveness, their stability through time, their collection time, their size, and the accuracy of a simple verification mechanism. We assess these properties on a large-scale dataset of 4,145,408 fingerprints composed of 216 attributes and collected from 1,989,365 browsers. We show that, by time-partitioning our dataset, more than 81.3% of our fingerprints are shared by a single browser. Although browser fingerprints are known to evolve, an average of 91% of the attributes of our fingerprints stay identical between two observations, even when separated by nearly six months. About their performance, we show that our fingerprints weigh a dozen of kilobytes and take a few seconds to collect. Finally, by processing a simple verification mechanism, we show that it achieves an equal error rate of 0.61%. We enrich our results with the analysis of the correlation between the attributes and their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.

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Combining Cryptography with EEG Biometrics

Computational Intelligence and Neuroscience ◽

10.1155/2018/1867548 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 15

Author(s):

Robertas Damaševičius ◽

Rytis Maskeliūnas ◽

Egidijus Kazanavičius ◽

Marcin Woźniak

Keyword(s):

Error Rate ◽

User Authentication ◽

Discrete Logarithm ◽

Security Analysis ◽

Experimental Results ◽

Biometric Authentication ◽

Equal Error Rate ◽

Eeg Data ◽

Authentication System ◽

Biometric Cryptosystem

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

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Bidirectional Attention for Text-Dependent Speaker Verification

Sensors ◽

10.3390/s20236784 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6784

Author(s):

Xin Fang ◽

Tian Gao ◽

Liang Zou ◽

Zhenhua Ling

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Cost Function ◽

Error Rate ◽

Speaker Verification ◽

Feature Learning ◽

Biometric Authentication ◽

Equal Error Rate ◽

Text Dependent Speaker Verification ◽

Target Speaker

Automatic speaker verification provides a flexible and effective way for biometric authentication. Previous deep learning-based methods have demonstrated promising results, whereas a few problems still require better solutions. In prior works examining speaker discriminative neural networks, the speaker representation of the target speaker is regarded as a fixed one when comparing with utterances from different speakers, and the joint information between enrollment and evaluation utterances is ignored. In this paper, we propose to combine CNN-based feature learning with a bidirectional attention mechanism to achieve better performance with only one enrollment utterance. The evaluation-enrollment joint information is exploited to provide interactive features through bidirectional attention. In addition, we introduce one individual cost function to identify the phonetic contents, which contributes to calculating the attention score more specifically. These interactive features are complementary to the constant ones, which are extracted from individual speakers separately and do not vary with the evaluation utterances. The proposed method archived a competitive equal error rate of 6.26% on the internal “DAN DAN NI HAO” benchmark dataset with 1250 utterances and outperformed various baseline methods, including the traditional i-vector/PLDA, d-vector, self-attention, and sequence-to-sequence attention models.

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Survey of Clustering Methods for Large Scale Dataset

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.13381344 ◽

2019 ◽

Vol 7 (5) ◽

pp. 1338-1344

Author(s):

Anupama Jawale ◽

Ganesh Magar

Keyword(s):

Large Scale ◽

Clustering Methods ◽

Large Scale Dataset

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Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

Computational Visual Media ◽

10.1007/s41095-021-0207-y ◽

2021 ◽

Author(s):

Jin Zhou ◽

Qing Zhang ◽

Jian-Hao Fan ◽

Wei Sun ◽

Wei-Shi Zheng

Keyword(s):

Large Scale ◽

Assessment Model ◽

Generic Model ◽

Small Subset ◽

Deep Convolutional Neural Networks ◽

Personal Taste ◽

Hinge Loss ◽

Novel Approach ◽

Large Scale Dataset ◽

Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

Journal of Imaging ◽

10.3390/jimaging7030050 ◽

2021 ◽

Vol 7 (3) ◽

pp. 50

Author(s):

Anselmo Ferreira ◽

Ehsan Nowroozi ◽

Mauro Barni

Keyword(s):

Large Scale ◽

State Of The Art ◽

Child Pornography ◽

Forensic Analysis ◽

Synthetic Image ◽

Image Detection ◽

Face Images ◽

Large Scale Dataset ◽

Scanned Images ◽

Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

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ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

International Journal of Computer Vision ◽

10.1007/s11263-021-01477-5 ◽

2021 ◽

Author(s):

Anil S. Baslamisli ◽

Partha Das ◽

Hoang-An Le ◽

Sezer Karaoglu ◽

Theo Gevers

Keyword(s):

Neural Network ◽

Large Scale ◽

State Of The Art ◽

Image Decomposition ◽

Natural Environments ◽

Decomposition Algorithms ◽

Ambient Light ◽

Fine Grained ◽

Large Scale Dataset ◽

Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

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Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

Remote Sensing ◽

10.3390/rs13050905 ◽

2021 ◽

Vol 13 (5) ◽

pp. 905

Author(s):

Chuyi Wu ◽

Feng Zhang ◽

Junshi Xia ◽

Yichen Xu ◽

Guoqing Li ◽

...

Keyword(s):

Damage Assessment ◽

Large Scale ◽

Binary Classification ◽

Open Data ◽

Building Damage ◽

Attention Mechanism ◽

Large Scale Dataset ◽

Data Program ◽

The Impact ◽

Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead

Cells ◽

10.3390/cells10051030 ◽

2021 ◽

Vol 10 (5) ◽

pp. 1030

Author(s):

Julie Lake ◽

Catherine S. Storm ◽

Mary B. Makarious ◽

Sara Bandres-Ciga

Keyword(s):

Neurodegenerative Diseases ◽

Large Scale ◽

Disease Diagnosis ◽

Open Science ◽

Future Directions ◽

The Road ◽

Current State ◽

Molecular Complexity ◽

Science Framework ◽

Lateral Sclerosis

Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.

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