Fingerprint Processing Research Articles

Scalable and robust unsupervised android malware fingerprinting using community-based network partitioning

The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building a scalable and obfuscation resilient similarity network infrastructure of malicious apps. Our approach is based on our proposed concept, namely malicious community, in which we consider malicious instances that share common features are the most likely part of the same malware family. Using this concept, we presumably assume that multiple similar Android apps with different authors are most likely to be malicious. Specifically, Cypider leverages this assumption for the detection of variants of known malware families and zero-day malicious apps. Cypider applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and possibly malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a one-class machine learning model for each malicious community. Besides, we proposed an enhanced Cypider framework, which requires less memory, ≈ x650%, and less time to build the similarity network, ≈ x700, compared to the original version, without affecting the fingerprinting performance of the framework. We introduce a systematic approach to locate the best threshold on different feature content vectors, which simplifies the overall detection process. Cypider shows excellent results by detecting 60%−80% coverage of the malware dataset in one detection iteration with higher precision 85%−99% in the detected malicious communities. On the other hand, the community fingerprints are promising as we achieved 86%, 93%, and 94% in the detection of the malware family, general malware, and benign apps respectively.

Fingerprint Matching Using Bozorth3 Algorithm and Parallel Computation on NVIDIA Compute Unified Device Architecture

This paper studied fingerprint matching employing Bozorth3 Algorithm for matching fingerprint and parallel computation employing NVIDIA Compute Unified Device Architecture (NVIDIA CUDA). The objective of this study obtains the percentage and time processing of matching fingerprints. In this study, the fingerprint matching is done with parallel computing is applied to the GPU (Graphics Processing Unit). GPU device used in this study is the CUDA (Compute Unified Device Architecture), which is an Application Programming Interface (API) developed by NVIDIA. The development of applications with fingerprint matching serial computing on CPU and parallel computing on GPU can be applied to the CUDA API. The results from this study can be found in the performance process on the CPU and GPU. The results of this research are the process on CUDA execution time is better than the execution time on the CPU, the process is done at both the computation is to find a match in the fingerprint value.

Scalable and robust unsupervised Android malware fingerprinting using community-based network partitioning

The daily amount of Android malicious applications (apps) targeting the app repositories is increasing, and their number is overwhelming the process of fingerprinting. To address this issue, we propose an enhanced Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building a scalable and obfuscation resilient similarity network infrastructure of malicious apps. Our approach is based on our proposed concept, namely malicious community, in which we consider malicious instances that share common features are the most likely part of the same malware family. Using this concept, we presumably assume that multiple similar Android apps with different authors are most likely to be malicious. Specifically, Cypider leverages this assumption for the detection of variants of known malware families and zero-day malicious apps. Cypider applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and possibly malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a one-class machine learning model for each malicious community. Besides, we proposed an enhanced Cypider framework, which requires less memory, ≈ x650%, and less time to build the similarity network, ≈ x700, compared to the original version, without affecting the fingerprinting performance of the framework. We introduce a systematic approach to locate the best threshold on different feature content vectors, which simplifies the overall detection process. Cypider shows excellent results by detecting 60−80% coverage of the malware dataset in one detection iteration with higher precision 85−99% in the detected malicious communities. On the other hand, the community fingerprints are promising as we achieved 86%, 93%, and 94% in the detection of the malware family, general malware, and benign apps, respectively.

Boyer-Moore String Matching Algorithm and SHA512 Implementation for Jpeg/exif File Fingerprint Compilation in DSA

Data integrity, authenticity and non-repudiation are security parameters that provided by Digital Signature Algorithm (DSA). Hash value is important element inside DSA to identify information data integrity using hash function to generate message digest. Jpeg/exif is image file format that produce by digital camera as in smartphone. Hardware technology development made image file have higher resolution than before. This condition made image file fingerprinting need more time to compile jpeg/exif fingerprint. This research purpose is to develop fingerprinting process for jpeg/exif file using Boyer-Moore string matching algorithm and SHA512. Research conducted in four stages. First stage is jpeg/exif file structure identification, second stage is segment content acquisition and hashing, third stage is image file modification experiments and last stage is jpeg/exif file fingerprint compilation. Obtained result shown that jpeg/exif file fingerprint comprises of three hash value from SOI segment, APP1 segment and SOF0 segment. The jpeg/exif file fingerprint can use to detect six types image modification there are image resizing, text addition, metadata modification, image resizing, image cropping and file file type conversion.

Sedentariness and Urinary Metabolite Profile in Type 2 Diabetic Patients, a Cross-Sectional Study

Recent findings indicate a significant association between sedentary (SED)-time and type 2 diabetes mellitus (T2DM). The aim of this study was to investigate whether different levels of SED-time could impact on biochemical and physiological processes occurring in sedentary and physically inactive T2DM patients. In particular, patients from the “Italian Diabetes and Exercise Study (IDES)_2 trial belonging to the first and fourth quartile of SED-time were compared. Urine samples were analyzed by comprehensive two-dimensional gas chromatography (GC × GC) with parallel detection by mass spectrometry and flame ionization detection (GC × 2GC-MS/FID). This platform enables accurate profiling and fingerprinting of urinary metabolites while maximizing the overall information capacity, quantitation reliability, and response linearity. Moreover, using advanced pattern recognition, the fingerprinting process was extended to untargeted and targeted features, revealing diagnostic urinary fingerprints between groups. Quantitative metabolomics was then applied to analytes of relevance for robust comparisons. Increased levels of glycine, L-valine, L-threonine, L-phenylalanine, L-leucine, L-alanine, succinic acid, 2-ketoglutaric acid, xylitol, and ribitol were revealed in samples from less sedentary women. In conclusion, SED-time is associated with changes in urine metabolome signatures. These preliminary results suggest that reducing SED-time could be a strategy to improve the health status of a large proportion of diabetic patients.

A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method

Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.

Fingerprint Identification using Bozorth and Boyer-Moore Algorithm

The process of fingerprint identification in the form of a binary image can be performed with a variety of algorithms to matching the fingerprint process. This study aims to determine the time processing for fingerprint detection using Bozorth and Boyer-Moore models. The result showed that both of the models could detection the fingerprint. This paper contribution is to compare and analyze Bozorth and Boyer-Moore algorithms. The comparison is performed by MATLAB simulation. The simulation results Boyer-Moore algorithm has faster identification process. On the other hand, Bozort algorithm has an advantage for identification of effect rotated or space translated fingerprints.

Современное состояние научных знаний и практики криминалистического исследования следов рук, образованных в условиях негативного влияния свойств объектов или действия факторов внешней среды

In the context of historical analysis the present-day state of scientific knowledge and practice of forensic processing of fingerprints formed under the negative objects properties or the action of environmental factors is considered. It is noted that today there is no uniform scientific and methodological pproach to the forensic processing of fingerprints on objects with criminally significant signs in science and practice that are particular demonstrations of their properties due to the special condition of the surface (oiliness, increased adhesion, etc.) or the action of environmental factors. Such complicated cases of the fingerprints formation, according to the author, require accounting of a larger number of circumstances taken into account in the detection and forensic study of these fingerprints. As part of the solution to this problem, modernization of the existing concept of fingerprints forensic processing is proposed, which will promote the theory and practice of dactyloscopy and fingerprint forensic examination in accordance with the modern state of scientific and technological progress.

Upaya Imigrasi dalam Penerapan Sanksi Pidana Terhadap Pengguna Dokumen Perjalanan Palsu

The purpose of this study is to find out how the forms of falsification of the Travel Documents of the Republic of Indonesia, how the application of criminal sanctions for the forgeries of the Travel Documents of the Republic of Indonesia according to positive Indonesian law and how immigration actions in overcoming the fraudulent Travel Documents of the Republic of Indonesia occur. The research method used in this research is descriptive analysis, data collection techniques with literature studies and interviews, the types of data are primary data and secondary data, while the data analysis using descriptive cumulative data analysis is descriptive. From the results of research that cases of forgery of Travel Documents of the Republic of Indonesia (passports), can be classified into four forms of forgery of Travel Documents of the Republic of Indonesia (passports): original documents obtained illegally (using false or incorrect data), original documents that have been subjected to changes, documents that are completely falsified (duplication), original documents used by others (Impostor). Articles used in the crime of forgery of passports are article 119, article 126, article 127, article 129. Countermeasures to prevent the falsification of Travel Documents of the Republic of Indonesia, such as by: Issuance of Electronic Passport (e-passport), Photo and fingerprinting process finger recording the applicant's data, interview process when the applicant submits a passport application, Provision of Passport Safety Features.

Food identification by high performance liquid chromatography fingerprinting and mathematical processing.

Food fingerprinting methods comprise a wide range of analytical strategies for obtaining analytical signals such as spectra or chromatograms that can be related to the composition of foodstuffs. Mathematical processing of fingerprints in such signals allows some foodstuffs to be characterized and/or authenticated. This paper deals at length with food identification by High Performance Liquid Chromatography in combination with mathematical processing. Also, it discusses existing approaches to the integrated acquisition of chromatographic signals and chemometric processing of chromatographic data, and illustrates the uses of fingerprinting methods for different types of foodstuffs.

Generating a Cancellable Fingerprint using Matrices Operations and Its Fingerprint Processing Requirements

Cancellable fingerprint uses transformed or intentionally distorted biometric data instead of the original biometric data for identifying person. When a set of biometric data is found to be compromised, they can be discarded, and a new set of biometric data can be regenerated. This initial principal is identical with a non-invertible concept in matrices operations. In matrix domain, a matrix cannot be transformed into its original form if it meets several requirements such as non-square form matrix, consist of one zero row/column, and no row as multiple of another row. These conditions can be acquired by implementing three matrix operations using Kronecker Product (KP) operation, Elementary Row Operation (ERO), and Inverse Matrix (INV) operation. KP is useful to produce a non-square form matrix, to enlarge the size of matrix, to distinguish and disguise the element of matrix by multiplying each of elements of the matrix with a particular matrix. ERO can be defined as multiplication and addition force to matrix rows. INV is utilized to transform one matrix to another one with a different element or form as a reciprocal matrix of the original. These three matrix operations should be implemented together in generating the cancellable feature to robust image. So, if once three conditions are met by imposter, it is impossible to find the original image of the fingerprint. The initial aim of these operations is to camouflage the original look of the fingerprint feature into an abstract-look to deceive an un-authorized personal using the fingerprint irresponsibly. In this research, several fingerprint processing steps such as fingerprint pre-processing, core-point identification, region of interest, minutiae extration, etc; are determined to improve the quality of the cancellable feature. Three different databases i.e. FVC2002, FVC2004, and BRC are utilized in this work.

Anti-fingerprint properties of engineering surfaces: a review

ABSTRACTVarious applications require surfaces with anti-fingerprint properties. This article reviews the surface properties that govern the fingerprinting process. The achievement of surfaces free of fingerprints deals with the construction of amphiphobic surfaces that repel water and oil. Based on wetting theories, two key parameters (Surface energy and Roughness) show significant effects on surface wettability. We address in this paper, the basic requirements and strategies of designing and manufacturing amphiphobic surfaces using different substrates. The synergetic effect of low surface energy and functional surface morphology leads to reduced wettability, resulting in anti-fingerprint properties. The trends of future investigations and fundamental conclusions are drawn. The remaining challenges and future outlook of this field are also highlighted.

Deep Learning Based Pipeline for Fingerprinting Using Brain Functional MRI Connectivity Data

In this work we describe an appropriate pipeline for using deep-learning as a form of improving the brain functional connectivity-based fingerprinting process which is based in functional Magnetic Resonance Imaging (fMRI) data-processing results. This pipeline approach is mostly intended for neuroscientists, biomedical engineers, and physicists that are looking for an easy form of using fMRI-based Deep-Learning in identifying people, drastic brain alterations in those same people, and/or pathologic consequences to people’s brains. Computer scientists and engineers can also gain by noticing the data-processing improvements obtained by using the here-proposed pipeline. With our best approach, we obtained an average accuracy of 0.3132 ± 0.0129 and an average validation cost of 3.1422 ± 0.0668, which clearly outperformed the published Pearson correlation approach performance with a 50 Nodes parcellation which had an accuracy of 0.237.

Optimizing fingerprinting experiments for parameter identification: Application to spin systems

We introduce the optimal fingerprinting process which is aimed at accurately identifying the parameters which characterize the dynamics of a physical system. A database is first built from the time evolution of an ensemble of dynamical systems driven by a specific field, which is designed by optimal control theory to maximize the efficiency of the recognition process. Curve fitting is then applied to enhance the precision of the identification. As an illustrative example, we consider the estimation of the relaxation parameters of a spin-1/2 particle. The experimental results are in good accord with the theoretical computations. We show on this example a physical limit of the estimation process.

Real Time Wi-Fi Indoor Positioning System Based on RSSI Measurements: A Distributed Load Approach with the Fusion of Three Positioning Algorithms

This paper investigates the effectiveness and applicability of fusing three wireless positioning algorithms to determine the positions and track nomadic sensor nodes in real environment conditions. We fuse finger printing and atomic multilateration processes to give the system the best feasible region and to ensure that the later does not sway much due to accumulative errors. The extended Kalman filter is then used for refining the estimated position in near real time. The paper further assesses the response speed and the accuracy of estimating the position of the nomadic nodes with a prudent distribution of the processing load.

Detecting rogue attacks on commercial wireless Insteon home automation systems

The Internet of Things (IoT) and commercial wireless home automation applications are expanding as technical capability evolves and implementation costs continue to decrease. However, many home automation devices lack robust security and are vulnerable to a multitude of bit-level attacks. This was highlighted during the first successful Insteon network intrusion demonstration that occurred at DEF CON 23 using a Software Defined Radio (SDR) with YARD Stick One devices. In response, Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting is introduced here as a counter-hacking approach for augmenting network bit-level Identity (ID) authentication using Physical Layer (PHY) waveform features. An RF-DNA Fingerprinting process is adopted here and applied to wireless Insteon home automation devices. Rogue device detection is addressed using a Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) ID verification process. Rogue assessments include attacks by like-model Insteon Switch (IS) devices and YARD Stick One SDR devices programmed to present actual (false) bit-level credentials for authorized Insteon devices while functionally controlling the state of an unprotected (no RF-DNA discrimination) targeted end point device. Device classification and Rogue Rejection Rate (RRR) performance is assessed using Time Domain (TD) and Slope-Based Frequency Shift Keyed (SB-FSK) Fingerprinting with features extracted from a variant (data dependent) signal response region. The Rogue Rejection Rate (RRR) for SB-FSK Fingerprinting was superior to TD Fingerprinting and included RRR ≈ 95% for 25 like-model IS attacks and RRR ≈ 100% for 36 YARD Stick One SDR attacks. SB-FSK Fingerprinting is more computationally efficient (70% fewer features) than TD Fingerprinting and provides an added benefit of being implementable using features extracted from variant data dependent FSK signal response regions.

Use of optical vortices for the compression of fingerprint recognition systems

In this article, an algorithm is presented for the processing and verification of fingerprints by analyzing optical vortices present in the orientation of the fringes of the fingerprint. The algorithm presents a compression factor of 1200, but is highly susceptible to the presence of noise.

Design and Their Efficiency of the Camera Equipment with Macro Lens and Multi-Lighting Source Module for Close-Up Picture of a Fingerprint

기존의 지문촬영 방법은 접사렌즈를 장착한 디지털 카메라에 적절한 필터와 법광원을 활용하 여 삼각대 등으로 고정한 후 주변의 상황에 따라 카메라를 세팅하는 방식이었다. 그러므로 사용 상 불편함이 많았을 뿐 아니라, 양호한 결과를 얻기가 매우 어려웠다. 본 연구에서 저자들은 이러한 문제점 을 개선하고자 범죄현장에서 접사촬영 시 필요한 모든 요소를 통합하여 어떤 조건하에서도 최적의 조건을 구현할 수 있도록 사진촬영이 가능한 접사 촬영용 복합광원 모듈 장비를 설계하고 효율성을 확인 하였다. 본 장비는 촬영에 필요한 기기를 원통형으로 제작하여 주변 빛에 영향을 받지 않도록 촬영 대상물 주변을 암실화하고, 광원의 각도를 조절하여(0 ~ 90˚) 측광 및 직광 등 다양한 각도로 광원의 조사가 가능하며 4개의 LED를 사용하여 기기 내부에서 20여 가지의 혼합색을 구현할 수 있도록 하였다. 그리고 촬영된 지문현출 색상에 따라서 AFIS에 의한 평균(Ave.) 매칭 및 특징점수에 관한 개선비율(%) 은 기존 방법(A)보다 신규 개발된 방법(B)이 녹색(G)에서 매칭점수는 3%, 특징점 개수는 8%을 보였으나 노란색(Y)에서 매칭점수는 22%, 특징점수는 약 40%을 각각 나타내었다. 이 같은 결과로부터 동일 한 목적의 기존장비와 성능을 비교한 결과, 현저하게 개선된 효율성을 확인하였다. 결론적으로 본 지문 촬영 접사용 복합광원 조절 신규모듈은 일선 과학수사관들의 지문처리 업무를 효율적으로 극대화할 수 있을 것이다.The conventional close-up picture for fingerprints in case of existing method was a method of setting digital camera according to the shooting environment equipped macro lens with appropriate filter and forensic light source after fixing the digital camera with a tripod. Therefore, there were many inconveniences in use and very difficult to obtain good results. In this study, the authors are designed and ensure efficiency the compound light source module by equipped all elements for close up picture can provide optimal results under any conditions at the crime scenes. The developed module designed cylindrical to be unaffected by outside lighting source by make dark room around the subject. The adjustable angle (0 ~ 90 o ) function for irradiating in various angles and using 4 LEDs to realize 20 mixed-color inside the module. And the improvement ratio (%) of the average (Ave.) matching point and the number of minutiae using developed module showed better result than existing method by AFIS according to the colors of fingerprint development powder. In the green (G), the matching point was 3% and the number of minutiae was improved about 8%. But in the yellow (Y), the matching point was 22% and the number of minutiae was improved about 40%, respectively. From these results, the remarkably improved efficiencies were confirmed as compared the performance with existing device. In conclusion, the new developed module will be able to maximize the fingerprint processing tasks of the frontline forensic investigators.

Holistic processing of fingerprints by expert forensic examiners

Holistic processing is often characterized as a process by which objects are perceived as a whole rather than a compilation of individual features. This mechanism may play an important role in the development of perceptual expertise because it allows for rapid integration across image regions. The present work explores whether holistic processing is present in latent fingerprint examiners, who compare fingerprints collected from crime scenes against a set of standards taken from a suspect. We adapted a composite task widely used in the face recognition and perceptual expertise literatures, in which participants were asked to match only a particular half of a fingerprint with a previous image while ignoring the other half. We tested both experts and novices, using both upright and inverted fingerprints. For upright fingerprints, we found weak evidence for holistic processing, but with no differences between experts and novices with respect to holistic processing. For inverted fingerprints, we found stronger evidence of holistic processing, with weak evidence for differences between experts and novices. These relatively weak holistic processing effects contrast with robust evidence for holistic processing with faces and with objects in other domains of perceptual expertise. The data constrain models of holistic processing by demonstrating that latent fingerprint experts and novices may not substantively differ in terms of the amount of holistic processing and that inverted stimuli actually produced more evidence for holistic processing than upright stimuli. Important differences between the present fingerprint stimuli and those in the literature include the lack of verbal labels for experts and the absence of strong vertical asymmetries, both of which might contribute to stronger holistic processing signatures in other stimulus domains.

Sparse coding based orientation estimation for latent fingerprints

Fingerprint orientations are often used to describe the ridge flow patterns, providing useful features for further fingerprint processing and recognition. Although significant advances have been achieved for orientation estimation, it is still challenging to reliably estimate the orientations for latent fingerprints, which are usually of poor quality with unclear ridge structure and various overlapping patterns. Motivated by the recent success of sparse coding in image denoising and reconstruction, this paper proposes an orientation estimation algorithm based on dictionary learning and sparse coding for latent fingerprints. First, a texture image is obtained by decomposition of latent image with a total variation model. The structured noise is greatly reduced from the texture image. Second, we propose a multi-scale sparse coding method for iterative estimation of local ridge orientations on the texture image. Multi-scale dictionaries are learned from the orientation fields of good quality fingerprints to capture the prior knowledge of various orientation patterns, and sparse coding is iteratively applied with the increase of patch sizes to correct the corrupted orientations of latent fingerprint. The proposed algorithm can work well to reduce the effect of various noise and restore the corrupted orientations while maintain the details of singular region. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm.