Relevant bibliographies by topics / Spoofed fingerprints

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Spoofed fingerprints'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Spoofed fingerprints"

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Nastasiu, Dragoș, Răzvan Scripcaru, Angela Digulescu, Cornel Ioana, Raymundo De Amorim, Nicolas Barbot, Romain Siragusa, Etienne Perret, and Florin Popescu. "A New Method of Secure Authentication Based on Electromagnetic Signatures of Chipless RFID Tags and Machine Learning Approaches." Sensors 20, no. 21 (November 9, 2020): 6385. http://dx.doi.org/10.3390/s20216385.

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In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss.
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NIKAM, SHANKAR BHAUSAHEB, and SUNEETA AGARWAL. "CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION." International Journal of Image and Graphics 09, no. 02 (April 2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.

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Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.
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F.W. Onifade, Olufade, Paul Akinde, and Folasade Olubusola Isinkaye. "Circular Gabor wavelet algorithm for fingerprint liveness detection." Journal of Advanced Computer Science & Technology 9, no. 1 (January 11, 2020): 1. http://dx.doi.org/10.14419/jacst.v9i1.29908.

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Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.
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Agarwal, Rohit, A. S. Jalal, and K. V. Arya. "A review on presentation attack detection system for fake fingerprint." Modern Physics Letters B 34, no. 05 (February 3, 2020): 2030001. http://dx.doi.org/10.1142/s021798492030001x.

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Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.
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Babikir Adam, Edriss Eisa, and Sathesh. "Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View." Journal of ISMAC 3, no. 1 (March 1, 2021): 16–30. http://dx.doi.org/10.36548/jismac.2021.1.002.

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Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.
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Lee, Fu-Yuan, and Shiuhpyng Shieh. "Defending against spoofed DDoS attacks with path fingerprint." Computers & Security 24, no. 7 (October 2005): 571–86. http://dx.doi.org/10.1016/j.cose.2005.03.005.

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Leghari, Mehwish, Shahzad Memon, Lachhman Das Dhomeja, Akhtar Hussain Jalbani, and Asghar Ali Chandio. "Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics." Computers 10, no. 2 (February 7, 2021): 21. http://dx.doi.org/10.3390/computers10020021.

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The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.
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Sekulska-Nalewajko, Joanna, Jarosław Gocławski, and Dominik Sankowski. "The Detection of Internal Fingerprint Image Using Optical Coherence Tomography." Image Processing & Communications 22, no. 4 (December 1, 2017): 59–72. http://dx.doi.org/10.1515/ipc-2017-0022.

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Abstract Recently, optical coherence tomography (OCT) has been tested as a contactless technique helpful for damaged or spoofed fingerprint recovery. Three dimensional OCT images cover the range from the skin surface to papillary region in upper dermis. The proposed method extracts from cross-sections of volumetric images (B-scans) high intensity ridges in both air-epidermis and dermis-epidermis interfaces. The extraction is based on the localisation of two OCT signal peaks corresponding to these edges. The borders are spline smoothed in two orthogonal planes of the image space. The result images are presented and compared with camera views.
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Verma, Akhilesh, Vijay Kumar Gupta, Savita Goel, Akbar, Arun Kumar Yadav, and Divakar Yadav. "Modeling Fingerprint Presentation Attack Detection Through Transient Liveness Factor-A Person Specific Approach." Traitement du Signal 38, no. 2 (April 30, 2021): 299–307. http://dx.doi.org/10.18280/ts.380206.

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A self-learning, secure and independent open-set solution is essential to be explored to characterise the liveness of fingerprint presentation. Fingerprint spoof presentation classified as live (a Type-I error) is a major problem in a high-security establishment. Type-I error are manifestation of small number of spoof sample. We propose to use only live sample to overcome above challenge. We put forward an adaptive ‘fingerprint presentation attack detection’ (FPAD) scheme using interpretation of live sample. It requires initial high-quality live fingerprint sample of the concerned person. It uses six different image quality metrics as a transient attribute from each live sample and record it as ‘Transient Liveness Factor’ (TLF). Our study also proposes to apply fusion rule to validate scheme with three outlier detection algorithms, one-class support vector machine (SVM), isolation forest and local outlier factor. Proposed study got phenomenal accuracy of 100% in terms of spoof detection, which is an open-set method. Further, this study proposes and discuss open issues on person specific spoof detection on cloud-based solutions.
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Chugh, Tarang, and Anil K. Jain. "Fingerprint Spoof Detector Generalization." IEEE Transactions on Information Forensics and Security 16 (2021): 42–55. http://dx.doi.org/10.1109/tifs.2020.2990789.

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Dissertations / Theses on the topic "Spoofed fingerprints"

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Vrábľová, Žofia. "Pokročilé generování artefaktů falzifikátů do syntetických otisků prstů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445551.

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The goal of this thesis is to extend the application for spoof effects generation into synthetic fingerprints with the possibility of generation of two new spoof effects together with annotations of generated damages. Spoof effects chosen for this thesis are areas with lower clarity and defects in spoof material. Those effects were analyzed, methods to generate those effects were designed and then implemented. According to testing, generation of two new added spoof effects led to reduction in quality of fingerprint images, as well as the value of the similarity score determined during identification. In comparison with the original solution, the quality of the fingerprints decreased more in the extended solution, the similarity score in the generation of separate spoof effect decreased overall approximately equally.
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Kanich, Ondřej. "Výzkum v oblasti simulací poškození otisku prstu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-412602.

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Cílem této práce je vyvinout metody simulací poškozování otisků prstů. V první části je kladen důraz na shrnutí stávajících znalostí v oblasti generování syntetických otisků prstů a jejich poškozování. Dále jsou uvedeny informace o otiscích prstů obecně, jejich rozpoznávání a vlivy, které otisky poškozují, včetně onemocnění kůže. Práce obsahuje návrh a implementaci aplikace SyFDaS pro generování a modulární poškozování otisků prstů. Další částí je popis metod pro poškozování vlivem průtahového režimu, zúženého snímače, poškozeného snímače, přítlaku a vlhkosti, zkreslení pokožky, bradavic, atopického ekzému a lupénky. Dále je analyzováno několik dalších typů poškození včetně falzifikátů otisků prstů. Celkově je uvedeno 43 základních poškození, která jsou vizuálně verifikována. Díky kombinování poškození je využito 1 171 typů poškození a vygenerováno 348 300 obrázků otisků prstů, které jsou vyhodnoceny čtyřmi různými metodami posuzování kvality.
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Book chapters on the topic "Spoofed fingerprints"

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Salman, Ammar S., and Odai S. Salman. "Spoofed/Unintentional Fingerprint Detection Using Behavioral Biometric Features." In Advances in Intelligent Systems and Computing, 459–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52243-8_33.

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Franco, Annalisa, and Davide Maltoni. "Fingerprint Synthesis and Spoof Detection." In Advances in Biometrics, 385–406. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-921-7_20.

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Jang, Han-Ul, Hak-Yeol Choi, Dongkyu Kim, Jeongho Son, and Heung-Kyu Lee. "Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks." In Information Science and Applications 2017, 331–38. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4154-9_39.

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Chang, Shoude, Kirill Larin, Youxin Mao, Wahab Almuhtadi, and Costel Flueraru. "Fingerprint Spoof Detection By NIR Optical Analysis." In State of the art in Biometrics. InTech, 2011. http://dx.doi.org/10.5772/19453.

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Purohit, Himanshu, and Pawan K. Ajmera. "Contemporary Biometric System Design." In Advances in Computer and Electrical Engineering, 292–324. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2772-6.ch016.

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Individual's Identity Authentication depends on physical traits like face, iris, and fingerprint, etc., or behavioral traits like voice and signature. With the rapid advancement in the field of biometrics, multimodal biometric systems are replacing unimodal biometric systems. As the application of molecular biometric system removes certain errors like noisy data, interclass variations, spoof attacks, and unacceptable error rates as compared to unimodal biometric systems. Even the possibilities of multiple scenarios present in multimodal biometric systems are quite helpful for the consolidation of information using different levels of fusion. In this chapter, the authors try to analyze the technological change which is present due to growing field of biometrics with artificial intelligence and undergone a thorough research for multimodal biometric systems for effective authentication purpose. This study is quite helpful for getting different perception for the use of biometrics as a highest level of network security due to the fusion of many different modalities.
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Conference papers on the topic "Spoofed fingerprints"

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Arora, Shefali, Naman Maheshwari, and MPS Bhatia. "Spoofed Fingerprint Detection Based on Time Series Fingerprint Image Analysis." In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). IEEE, 2018. http://dx.doi.org/10.1109/icsccc.2018.8703334.

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Kanich, Ondrej, Martin Drahansky, and Martin Mezl. "Use of creative materials for fingerprint spoofs." In 2018 International Workshop on Biometrics and Forensics (IWBF). IEEE, 2018. http://dx.doi.org/10.1109/iwbf.2018.8401565.

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Nixon, Kristin A., and Robert K. Rowe. "Multispectral fingerprint imaging for spoof detection." In Defense and Security, edited by Anil K. Jain and Nalini K. Ratha. SPIE, 2005. http://dx.doi.org/10.1117/12.606643.

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Nikam, Shankar Bhausaheb, and Suneeta Agarwal. "Texture and Wavelet-Based Spoof Fingerprint Detection for Fingerprint Biometric Systems." In 2008 First International Conference on Emerging Trends in Engineering and Technology. IEEE, 2008. http://dx.doi.org/10.1109/icetet.2008.134.

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Gajawada, Rohit, Additya Popli, Tarang Chugh, Anoop Namboodiri, and Anil K. Jain. "Universal Material Translator: Towards Spoof Fingerprint Generalization." In 2019 International Conference on Biometrics (ICB). IEEE, 2019. http://dx.doi.org/10.1109/icb45273.2019.8987320.

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Chugh, Tarang, Kai Cao, and Anil K. Jain. "Fingerprint spoof detection using minutiae-based local patches." In 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2017. http://dx.doi.org/10.1109/btas.2017.8272745.

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Anusha, B. V. S., Sayan Banerjee, and Subhasis Chaudhuri. "DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093397.

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Chugh, Tarang, and Anil K. Jain. "Fingerprint Spoof Detection: Temporal Analysis of Image Sequence." In 2020 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2020. http://dx.doi.org/10.1109/ijcb48548.2020.9304921.

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Junfei, Shao, Shi Guangshun, Li Yue, Li Yuxuan, and Qiu Demin. "Partial Spoof Fingerprint Detection with a Hierarchical Scheme." In 2018 IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2018. http://dx.doi.org/10.1109/auteee.2018.8720810.

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Pereira, Luis Filipe A., Hector N. B. Pinheiro, Jose Ivson S. Silva, Anderson G. Silva, Thais M. L. Pina, George D. C. Cavalcanti, Tsang Ing Ren, and Joao Paulo N. de Oliveira. "A fingerprint spoof detection based on MLP and SVM." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252582.

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