Relevant bibliographies by topics / Spoof Detection

Academic literature on the topic 'Spoof Detection'

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Published: 14 March 2023

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Journal articles on the topic "Spoof Detection"

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Mittal, Abhishek, Pravneet Kaur, and Dr Ashish Oberoi. "Hybrid Algorithm for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 1028–37. http://dx.doi.org/10.22214/ijraset.2022.40452.

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Abstract: The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that the introduced method performs more efficiently in comparison with the conventional techniques with regard to accuracy. Keywords: Face Spoof, KNN, Hybrid Classifier, GLCM
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Singh, Km Priyanka, Dr Pushpneel Verma, and Ajay Singh. "Technique of Face Spoof Detection using Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1435–38. http://dx.doi.org/10.22214/ijraset.2022.46847.

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Abstract: Face detection is one in every of the foremost relevent application of image processing and biometric system. Artificial neural networks (ANN) are utilized in the sphere of image processing and pattern recognition. For the recognition and detection of spoofed and non-spoofed images, face spoof approach was proposed. Earlier presented support vector machine classification model is used for the detection of spoofed or non-spoofed images. within the earlier research, SVM based approach was proposed to detect the face spoof. The face spoof detection approaches involves two stages. The initial stage includes feature extraction and second stage includes classification. The features are extracted using Eigen based system. The classification is performed through SVM classifier. within the proposed approach, the KNN classifier is used in place of SVM classifier for improving the accuracy of the face spoof discovery. The performance of the proposed algorithm and also the earlier algorithm is analyzed through some comparisons among them in terms of precision and execution time.
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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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Mittal, Abhishek. "Hybrid Classification for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1732–39. http://dx.doi.org/10.22214/ijraset.2021.39085.

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Abstract: ML (machine learning) is consisted of a method of recognizing face. This technique is useful for the attendance system. Two sets are generated for testing and training phases in order to segment the image, to extract the features and develop a dataset. An image is considered as a testing set; the training set is contrasted when it is essential to identify an image. An ensemble classifier is implemented to classify the test images as recognized or non-recognized. The ensemble algorithm fails to acquire higher accuracy as it classifies the data in two classes. Thus, GLCM (Grey Level Co-occurrence Matrix) is projected for analyzing the texture features in order to detect the face. The attendance of the query image is marked after detecting the face. The simulation outcomes revealed the superiority of the projected technique over the traditional methods concerning accuracy. Keywords: DWT, GLCM, KNN, Decision Tree
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Arunalatha, G., and M. Ezhilarasan. "Fingerprint Spoof Detection Using Quality Features." International Journal of Security and Its Applications 9, no. 10 (October 31, 2015): 83–94. http://dx.doi.org/10.14257/ijsia.2015.9.10.07.

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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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Suvarchala, P. V. L., and S. Srinivas Kumar. "Feature Set Fusion for Spoof Iris Detection." Engineering, Technology & Applied Science Research 8, no. 2 (April 19, 2018): 2859–63. http://dx.doi.org/10.48084/etasr.1859.

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Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.
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Kaur, Ramandeep, and P. S. "Techniques of Face Spoof Detection: A Review." International Journal of Computer Applications 164, no. 1 (April 17, 2017): 29–33. http://dx.doi.org/10.5120/ijca2017913569.

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Balamurali, K., S. Chandru, Muhammed Sohail Razvi, and V. Sathiesh Kumar. "Face Spoof Detection Using VGG-Face Architecture." Journal of Physics: Conference Series 1917, no. 1 (June 1, 2021): 012010. http://dx.doi.org/10.1088/1742-6596/1917/1/012010.

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Di Wen, Hu Han, and Anil K. Jain. "Face Spoof Detection With Image Distortion Analysis." IEEE Transactions on Information Forensics and Security 10, no. 4 (April 2015): 746–61. http://dx.doi.org/10.1109/tifs.2015.2400395.

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Dissertations / Theses on the topic "Spoof Detection"

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TOOSI, AMIRHOSEIN. "Feature Fusion for Fingerprint Liveness Detection." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711594.

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For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s ridge-valley patterns. Due to the criticality of this threat, it is crucial to develop countermeasure methods capable of facing and preventing these kind of attacks. The most effective counter–spoofing methods are those trying to distinguish between a "live" and a "fake" fingerprint before it is actually submitted to the recognition system. According to the technology used, these methods are mainly divided into hardware and software-based systems. Hardware-based methods rely on extra sensors to gain more pieces of information regarding the vitality of the fingerprint owner. On the contrary, software-based methods merely rely on analyzing the fingerprint images acquired by the scanner. Software-based methods can then be further divided into dynamic, aimed at analyzing sequences of images to capture those vital signs typical of a real fingerprint, and static, which process a single fingerprint impression. Among these different approaches, static software-based methods come with three main benefits. First, they are cheaper, since they do not require the deployment of any additional sensor to perform liveness detection. Second, they are faster since the information they require is extracted from the same input image acquired for the identification task. Third, they are potentially capable of tackling novel forms of attack through an update of the software. The interest in this type of counter–spoofing methods is at the basis of this dissertation, which addresses the fingerprint liveness detection under a peculiar perspective, which stems from the following consideration. Generally speaking, this problem has been tackled in the literature with many different approaches. Most of them are based on first identifying the most suitable image features for the problem in analysis and, then, into developing some classification system based on them. In particular, most of the published methods rely on a single type of feature to perform this task. Each of this individual features can be more or less discriminative and often highlights some peculiar characteristics of the data in analysis, often complementary with that of other feature. Thus, one possible idea to improve the classification accuracy is to find effective ways to combine them, in order to mutually exploit their individual strengths and soften, at the same time, their weakness. However, such a "multi-view" approach has been relatively overlooked in the literature. Based on the latter observation, the first part of this work attempts to investigate proper feature fusion methods capable of improving the generalization and robustness of fingerprint liveness detection systems and enhance their classification strength. Then, in the second part, it approaches the feature fusion method in a different way, that is by first dividing the fingerprint image into smaller parts, then extracting an evidence about the liveness of each of these patches and, finally, combining all these pieces of information in order to take the final classification decision. The different approaches have been thoroughly analyzed and assessed by comparing their results (on a large number of datasets and using the same experimental protocol) with that of other works in the literature. The experimental results discussed in this dissertation show that the proposed approaches are capable of obtaining state–of–the–art results, thus demonstrating their effectiveness.
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Pedersen, Øystein Aas. "Detecting Wireless Identity Spoofs in Urban Settings, Based on Received Signal Strength Measurements." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for telematikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11127.

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The most common gateway for executing attacks in 802.11 networks are the MACspoofing attack. Current Todays Wireless IDS implements different methods todetect MAC spoofing, but are particularly interested in using methods that arebased on characteristics that are considered unspoofable. One such characteristicis the received signal strength (RSS). Current research are often tested in officeenvironments only, and this work aims to test how the methods work in WirelessTrondheim’s urban environment. To research the effects, a wireless sensor networkwas made. A framework for treating captured data from the sensor network wasdeveloped that can be augmented with various detection methods for 802.11 basednetworks. A RSS detection method has been developed and tested with real testdata from an urban environment. A RSS based detection method was tested, andthe results depicts the challenges of using such methods in an urban environment.Results also show that existing statistically based RSS methods would work poorlyin such environments.
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Loffreno, Michele. "Computer Vision and Machine Learning for a Spoon-feeding Robot : A prototype solution based on ABB YuMi and an Intel RealSense camera." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182503.

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A lot of people worldwide are affected by limitations and disabilities that make it hard to do even essential actions and everyday tasks, such as eating. The impact of robotics on the lives of elder people or people having any kind of inability, which makes it hard everyday actions as to eat, was considered. The aim of this thesis is to study the implementation of a robotic system in order to achieve an automatic feeding process. Different kinds of robots and solutions were taken into account, for instance, the Obi and the prototype realized by the Washington University. The system considered uses an RGBD camera, an Intel RealSense D400 series camera, to detect pieces of cutlery and food on a table and a robotic arm, an ABB-YuMi, to pick up the identified objects. The spoon detection is based on the pre-trained convolutional neural network AlexNet provided by MATLAB. Two detectors were implemented. The first one can detect up to four different objects (spoon, plate, fork and knife), the second one can detect only spoon and plate. Different algorithms based on morphology were tested in order to compute the pose of the objects detected. RobotStudio was used to establish a connection between MATLAB and the robot. The goal was to make the whole process as automated as possible. The neural network trained on two objects reached 100% of accuracy during the training test. The detector based on it was tested on the real system. It was possible to detect the spoon and the plate and to draw a good centered boundary box. The accuracy reached can be considered satisfying since it has been possible to grasp a spoon using the YuMi based on a picture of the table. It was noticed that the lighting condition is the key factor to get a satisfying result or to miss the detection of the spoon. The best result was archived when the light is uniform and there are no reflections and shadows on the objects. The pictures which get a better result for the detection were taken in an apartment. Despite the limitations of the interface between MATLAB and the controller of the YuMi, a good level of automation was reached. The influence of lighting conditions in this setting was discussed and some practical suggestions and considerations were made.
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Books on the topic "Spoof Detection"

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Spook: A "nameless detective" novel. New York: Carroll & Graf Publishers, 2003.

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Spook: A "nameless detective" novel. Waterville, Me: Thorndike Press, 2003.

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The drowning spool. Waterville, Maine: Thorndike Press, 2014.

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Perry, Anne. Long Spoon Lane. Waterville, Me: Large Print Press, 2005.

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Perry, Anne. Long Spoon Lane. Waterville, Me: Thorndike Press, 2005.

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Perry, Anne. Long Spoon Lane. London: Headline, 2005.

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Perry, Anne. Long Spoon Lane: A novel. New York: Ballantine Books, 2005.

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Simenon, Georges. Maigret en het spook. [Amsterdam etc.]: Pandora, 2003.

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Perry, Anne. Long Spoon Lane: A novel / Anne Perry. New York: Ballantine Books, 2005.

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Knights of the sea: A grim tale of murder, politics, and spoon addiction. Sackville, N.B: Sybertooth, 2010.

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Book chapters on the topic "Spoof Detection"

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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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Abdullakutty, Faseela, Eyad Elyan, and Pamela Johnston. "Face Spoof Detection: An Experimental Framework." In Proceedings of the International Neural Networks Society, 293–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_25.

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Guo, Fanglu, and Tzi-cker Chiueh. "Sequence Number-Based MAC Address Spoof Detection." In Lecture Notes in Computer Science, 309–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11663812_16.

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Das, Tandra Rani, Sharad Hasan, S. M. Sarwar, Jugal Krishna Das, and Muhammad Arifur Rahman. "Facial Spoof Detection Using Support Vector Machine." In Advances in Intelligent Systems and Computing, 615–25. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4673-4_50.

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Eskandari, Maryam, and Omid Sharifi. "Designing Efficient Spoof Detection Scheme for Face Biometric." In Lecture Notes in Computer Science, 427–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94211-7_46.

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He, Zhaofeng, Zhenan Sun, Tieniu Tan, and Zhuoshi Wei. "Efficient Iris Spoof Detection via Boosted Local Binary Patterns." In Advances in Biometrics, 1080–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_109.

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Zheng, Mingchun, Shoubao Yang, Xianglan Piao, and Weifeng Sun. "Research on ARP Spoof Detection Strategies in Unreliable LAN." In Human Centered Computing, 216–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15554-8_18.

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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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Patil, Ankur T., Harsh Kotta, Rajul Acharya, and Hemant A. Patil. "Spectral Root Features for Replay Spoof Detection in Voice Assistants." In Speech and Computer, 504–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87802-3_46.

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Zhou, Ye, Jianwu Zhang, and Pengguo Zhang. "Spoof Speech Detection Based on Raw Cross-Dimension Interaction Attention Network." In Biometric Recognition, 621–29. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_63.

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Conference papers on the topic "Spoof Detection"

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Swaszek, Peter F., Richard J. Hartnett, and Kelly C. Seals. "APNT for GNSS Spoof Detection." In 2017 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2017. http://dx.doi.org/10.33012/2017.14957.

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Swaszek, Peter F., Richard J. Hartnett, and Kelly C. Seals. "GNSS Spoof Detection Using Passive Ranging." In 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016). Institute of Navigation, 2016. http://dx.doi.org/10.33012/2016.14778.

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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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Swaszek, Peter F., Richard J. Hartnett, and Kelly C. Seals. "GNSS Spoof Detection using Independent Range Information." In 2016 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2016. http://dx.doi.org/10.33012/2016.13457.

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Wu, Lei, and Ye Jiang. "Attentional Fusion TDNN for Spoof Speech Detection." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904136.

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Patil, Neha D., and Sujata V. Kadam. "Face spoof detection techniques: IDA and PCA." In 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016. http://dx.doi.org/10.1109/get.2016.7916823.

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Tapkir, Prasad A., Madhu R. Kamble, Hemant A. Patil, and Maulik Madhavi. "Replay Spoof Detection using Power Function Based Features." In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2018. http://dx.doi.org/10.23919/apsipa.2018.8659582.

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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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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, 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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