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Journal articles on the topic 'Spoof Detection'

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Author: Grafiati

Published: 14 March 2023

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1

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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2

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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3

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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5

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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7

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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8

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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9

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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10

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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11

Patel, Keyurkumar, Hu Han, and Anil K. Jain. "Secure Face Unlock: Spoof Detection on Smartphones." IEEE Transactions on Information Forensics and Security 11, no. 10 (October 2016): 2268–83. http://dx.doi.org/10.1109/tifs.2016.2578288.

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12

Balaji, Aadhithya, Varun. H.S., and Sikha. O.K. "Multimodal Fingerprint Spoof Detection Using White Light." Procedia Computer Science 78 (2016): 330–35. http://dx.doi.org/10.1016/j.procs.2016.02.066.

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13

Farmanbar, Mina, and Önsen Toygar. "Spoof detection on face and palmprint biometrics." Signal, Image and Video Processing 11, no. 7 (March 25, 2017): 1253–60. http://dx.doi.org/10.1007/s11760-017-1082-y.

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14

Ren, Yi, Jingjing Zhang, Xinxin Gao, Xin Zheng, Xinyu Liu, and Tie Jun Cui. "Active spoof plasmonics: from design to applications." Journal of Physics: Condensed Matter 34, no. 5 (November 11, 2021): 053002. http://dx.doi.org/10.1088/1361-648x/ac31f7.

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Abstract Spoof plasmonic metamaterials enable the transmission of electromagnetic energies with strong field confinement, opening new pathways to the miniaturization of devices for modern communications. The design of active, reconfigurable, and nonlinear devices for the efficient generation and guidance, dynamic modulation, and accurate detection of spoof surface plasmonic signals has become one of the major research directions in the field of spoof plasmonic metamaterials. In this article, we review recent progress in the studies on spoof surface plasmons with a special focus on the active spoof surface plasmonic devices and systems. Different design schemes are introduced, and the related applications including reconfigurable filters, high-resolution sensors for chemical and biological sensing, graphene-based attenuators, programmable and multi-functional devices, nonlinear devices, splitters, leaky-wave antennas and multi-scheme digital modulators are discussed. The presence of active SSPPs based on different design schemes makes it possible to dynamically control electromagnetic waves in real time. The promising future of active spoof plasmonic metamaterials in the communication systems is also speculated.

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15

Dave, Vani. "Spoof Detection Using Local Binary Pattern In Face." Jurnal Ilmu Komputer 13, no. 1 (April 29, 2020): 39. http://dx.doi.org/10.24843/jik.2020.v13.i01.p05.

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Spoofing attack is an attempt to acquire some other’s identity or access right by using a biometric evidence of authorized user. Among all biometric systems facial identity is one of the widely used method that is prone to such spoofing attacks using a simple photograph of the user. The paper focuses and takes the problem area of face spoofing attacks into account by detecting spoof faces and real faces. We are using the local binary pattern (LBP) for providing the solution of spoofing problem and with the help of these patterns we inspect primarily two types of attacks i.e. printed photograph and photos displayed using digital screen. For this, we will use the local database maintained by us having the images labeled as real and spoof for the data required. We conclude that local binary pattern will reduce the total error rate and will show the moderate output when used across a wide set of attack types. This will enhance the efficiency of the system for detection of spoofing by using the deep learning techniques

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Patil, Pooja R., and Subhash S. Kulkarni. "Survey of non-intrusive face spoof detection methods." Multimedia Tools and Applications 80, no. 10 (January 28, 2021): 14693–721. http://dx.doi.org/10.1007/s11042-020-10338-1.

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17

Ravindran, Swathika, and K. Geetha. "An Overview of Spoof Detection in ASV Systems." ECS Transactions 107, no. 1 (April 24, 2022): 1963–71. http://dx.doi.org/10.1149/10701.1963ecst.

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In current years, voice based application are used broadly in varied applications for speaker recognition. By and by, there is a wide work in the investigation of parodying and against mocking for Automatic Speaker Verification (ASV) framework. The current advancement within the ASV system ends up interest to secure these voice biometric systems for existent world applications. This paper provides the literature of spoofing detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Moreover, it conjointly summaries previous studies of spoofing attacks with stress on SS, VC, and replay alongside recent efforts to develop countermeasures for spoof speech detection and speech sound disorder tasks.

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18

Jiang, Yujia, and Xin Liu. "Spoof Fingerprint Detection based on Co-occurrence Matrix." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 8 (August 31, 2015): 373–84. http://dx.doi.org/10.14257/ijsip.2015.8.8.38.

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19

Kho, Jun Beom, Wonjune Lee, Heeseung Choi, and Jaihie Kim. "An incremental learning method for spoof fingerprint detection." Expert Systems with Applications 116 (February 2019): 52–64. http://dx.doi.org/10.1016/j.eswa.2018.08.055.

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20

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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21

Huszár, Viktor Dénes, and Vamsi Kiran Adhikarla. "Live Spoofing Detection for Automatic Human Activity Recognition Applications." Sensors 21, no. 21 (November 4, 2021): 7339. http://dx.doi.org/10.3390/s21217339.

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Human Activity Recognition (HAR) has become increasingly crucial in several applications, ranging from motion-driven virtual games to automated video surveillance systems. In these applications, sensors such as smart phone cameras, web cameras or CCTV cameras are used for detecting and tracking physical activities of users. Inevitably, spoof detection in HAR is essential to prevent anomalies and false alarms. To this end, we propose a deep learning based approach that can be used to detect spoofing in various fields such as border control, institutional security and public safety by surveillance cameras. Specifically, in this work, we address the problem of detecting spoofing occurring from video replay attacks, which is more common in such applications. We present a new database containing several videos of users juggling a football, captured under different lighting conditions and using different display and capture devices. We train our models using this database and the proposed system is capable of running in parallel with the HAR algorithms in real-time. Our experimental results show that our approach precisely detects video replay spoofing attacks and generalizes well, even to other applications such as spoof detection in face biometric authentication. Results show that our approach is effective even under resizing and compression artifacts that are common in HAR applications using remote server connections.

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Hassani, Ali, Jon Diedrich, and Hafiz Malik. "Monocular Facial Presentation–Attack–Detection: Classifying Near-Infrared Reflectance Patterns." Applied Sciences 13, no. 3 (February 3, 2023): 1987. http://dx.doi.org/10.3390/app13031987.

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This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under varied pose, position, and lighting for 80,000 unique frames. A panel of 13 texture classifiers are then benchmarked to verify the hypothesis. The experimental results are excellent. The material spectroscopy process enables a conventional MobileNetV3 network to achieve 0.8% average-classification-error rate, outperforming the selected state-of-the-art algorithms. This demonstrates the proposed imaging methodology generates extremely robust features.

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Alshareef, Norah, Xiaohong Yuan, Kaushik Roy, and Mustafa Atay. "A Study of Gender Bias in Face Presentation Attack and Its Mitigation." Future Internet 13, no. 9 (September 14, 2021): 234. http://dx.doi.org/10.3390/fi13090234.

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In biometric systems, the process of identifying or verifying people using facial data must be highly accurate to ensure a high level of security and credibility. Many researchers investigated the fairness of face recognition systems and reported demographic bias. However, there was not much study on face presentation attack detection technology (PAD) in terms of bias. This research sheds light on bias in face spoofing detection by implementing two phases. First, two CNN (convolutional neural network)-based presentation attack detection models, ResNet50 and VGG16 were used to evaluate the fairness of detecting imposer attacks on the basis of gender. In addition, different sizes of Spoof in the Wild (SiW) testing and training data were used in the first phase to study the effect of gender distribution on the models’ performance. Second, the debiasing variational autoencoder (DB-VAE) (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) was applied in combination with VGG16 to assess its ability to mitigate bias in presentation attack detection. Our experiments exposed minor gender bias in CNN-based presentation attack detection methods. In addition, it was proven that imbalance in training and testing data does not necessarily lead to gender bias in the model’s performance. Results proved that the DB-VAE approach (Amini, A., et al., Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure) succeeded in mitigating bias in detecting spoof faces.

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Akhtar, Zahid, and Gian Luca Foresti. "Face Spoof Attack Recognition Using Discriminative Image Patches." Journal of Electrical and Computer Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4721849.

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Face recognition systems are now being used in many applications such as border crossings, banks, and mobile payments. The wide scale deployment of facial recognition systems has attracted intensive attention to the reliability of face biometrics against spoof attacks, where a photo, a video, or a 3D mask of a genuine user’s face can be used to gain illegitimate access to facilities or services. Though several face antispoofing or liveness detection methods (which determine at the time of capture whether a face is live or spoof) have been proposed, the issue is still unsolved due to difficulty in finding discriminative and computationally inexpensive features and methods for spoof attacks. In addition, existing techniques use whole face image or complete video for liveness detection. However, often certain face regions (video frames) are redundant or correspond to the clutter in the image (video), thus leading generally to low performances. Therefore, we propose seven novel methods to find discriminative image patches, which we define as regions that are salient, instrumental, and class-specific. Four well-known classifiers, namely, support vector machine (SVM), Naive-Bayes, Quadratic Discriminant Analysis (QDA), and Ensemble, are then used to distinguish between genuine and spoof faces using a voting based scheme. Experimental analysis on two publicly available databases (Idiap REPLAY-ATTACK and CASIA-FASD) shows promising results compared to existing works.

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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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26

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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27

Rattani, Ajita, Walter J. Scheirer, and Arun Ross. "Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials." IEEE Transactions on Information Forensics and Security 10, no. 11 (November 2015): 2447–60. http://dx.doi.org/10.1109/tifs.2015.2464772.

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28

Darlow, Luke Nicholas, Leandra Webb, and Natasha Botha. "Automated spoof-detection for fingerprints using optical coherence tomography." Applied Optics 55, no. 13 (April 22, 2016): 3387. http://dx.doi.org/10.1364/ao.55.003387.

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29

Fei, Jianwei, Chengsheng Yuan, Qiang Zhang, Zhihua Xia, and Fei Gu. "Face spoof detection using feature map superposition and CNN." International Journal of Computational Science and Engineering 22, no. 2/3 (2020): 355. http://dx.doi.org/10.1504/ijcse.2020.10029396.

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Gu, Fei, Zhihua Xia, Jianwei Fei, Chengsheng Yuan, and Qiang Zhang. "Face spoof detection using feature map superposition and CNN." International Journal of Computational Science and Engineering 22, no. 2/3 (2020): 355. http://dx.doi.org/10.1504/ijcse.2020.107356.

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Nikam, Shankar Bhausaheb, and Suneeta Agarwal. "Local binary pattern and wavelet-based spoof fingerprint detection." International Journal of Biometrics 1, no. 2 (2008): 141. http://dx.doi.org/10.1504/ijbm.2008.020141.

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32

Pereira, L. F. A., H. N. B. Pinheiro, G. D. C. Cavalcanti, and Tsang Ing Ren. "Spatial surface coarseness analysis: technique for fingerprint spoof detection." Electronics Letters 49, no. 4 (February 2013): 260–61. http://dx.doi.org/10.1049/el.2012.4173.

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Jagdale, Prasad A., and Sudeep D. Thepade. "Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 8 (August 26, 2019): 19–22. http://dx.doi.org/10.17762/ijritcc.v7i8.5348.

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Nowadays the system which holds private and confidential data are being protected using biometric password such as finger recognition, voice recognition, eyries and face recognition. Face recognition match the current user face with faces present in the database of that security system and it has one major drawback that it never works better if it doesn’t have liveness detection. These face recognition system can be spoofed using various traits. Spoofing is accessing a system software or data by harming the biometric recognition security system. These biometric systems can be easily attacked by spoofs like peoples face images, masks and videos which are easily available from social media. The proposed work mainly focused on detecting the spoofing attack by training the system. Spoofing methods like photo, mask or video image can be easily identified by this method. This paper proposed a fusion technique where different features of an image are combining together so that it can give best accuracy in terms of distinguish between spoof and live face. Also a comparative study is done of machine learning classifiers to find out which classifiers gives best accuracy.

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Isaack Adidas Kamanga and Johanson Miserigodiasi Lyimo. "Anti-spoofing detection based on eyeblink liveness testing for iris recognition." International Journal of Science and Research Archive 7, no. 1 (September 30, 2022): 053–67. http://dx.doi.org/10.30574/ijsra.2022.7.1.0186.

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Iris pattern is the most stable biometric trait for personal identification. It is the only traits that can’t be used after a person death. Despite its stability and difficulty to spoof, it has found that presenting a high quality image of an iris one can spoof and gain an access. Furthermore, the use of video frames of an authorized personnel and the use of 3D models can cheat the system. This study aimed at presenting a solution to this problem by testing the liveness of an eye being scanned by an access control device. The algorithm works by additional process of detecting an eyeblink and background subtraction and correlation to assume liveness. For one to gain access, first an iris is scanned and identified, secondly if this iris is in the database before providing an access, an eyeblink is also sensed. If eyeblink is sensed an access is granted otherwise access is denied. An algorithm has been developed in MATLAB adopting an adaptive Canny method for edge detection. The proposed algorithm validates the user being scanned by two stages which are; Eyeblink detection, background subtraction and correlation. Testing on standard datasets of ZJU Eyeblink, ACASIA v3 and the TalkingFace databases showed show 96.47% accuracy.

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Kusuma, Indra Bayu, Arida Kartika, Tjokorda Agung Budi W, Kurniawan Nur Ramadhani, and Febryanti Sthevanie. "Image Spoofing Detection Using Local Binary Pattern and Local Binary Pattern Variance." International Journal on Information and Communication Technology (IJoICT) 4, no. 2 (April 2, 2019): 11. http://dx.doi.org/10.21108/ijoict.2018.42.134.

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Particularly in the field of biometric security using human face has been widely implemented in the real world. Currently the human face is one of the guidelines in the security system. Nowadays the challenge is how to detect data falsification; such an attack is called spoofing. Spoofing occurs when someone is trying to pretend to be someone else by falsifying the original data and then that person may gain illegal access and benefit him. For example one can falsify the face recognition system using photographs, video, masks or 3D models. In this paper image spoofing human face detection using texture analysis on input image is proposed. Texture analysis used in this paper is the Local Binary Pattern (LBP) and Local Binary Pattern Variance (LBPV). To classified input as original or spoof K-Nearest Neighbor (KNN) used. Experiment used 5761 spoofs and 3362 original from NUAA Imposter dataset. The experimental result yielded a best success rate of 87.22% in term of accuracy with configuration of the system using LBPV and histogram equalization with ratio 𝑅 = 7 and 𝑃 = 8.

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Kim, Seung-Hyun, Su-Min Jeon, and Eui Chul Lee. "Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal." Sensors 22, no. 8 (April 16, 2022): 3070. http://dx.doi.org/10.3390/s22083070.

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Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%.

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Nguyen, Dat Tien, Hyo Sik Yoon, Tuyen Danh Pham, and Kang Ryoung Park. "Spoof Detection for Finger-Vein Recognition System Using NIR Camera." Sensors 17, no. 10 (October 1, 2017): 2261. http://dx.doi.org/10.3390/s17102261.

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Liang, Yuxin, Chaoqun Hong, and Weiwei Zhuang. "Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks." International Journal of Computational Intelligence Systems 14, no. 1 (2021): 1396. http://dx.doi.org/10.2991/ijcis.d.210419.003.

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Pujol, Francisco A., María José Pujol, Carlos Rizo-Maestre, and Mar Pujol. "Entropy-Based Face Recognition and Spoof Detection for Security Applications." Sustainability 12, no. 1 (December 20, 2019): 85. http://dx.doi.org/10.3390/su12010085.

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Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches.

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Kamble, Madhu R., and Hemant A. Patil. "Detection of replay spoof speech using teager energy feature cues." Computer Speech & Language 65 (January 2021): 101140. http://dx.doi.org/10.1016/j.csl.2020.101140.

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Chintha, Akash, Bao Thai, Saniat Javid Sohrawardi, Kartavya Bhatt, Andrea Hickerson, Matthew Wright, and Raymond Ptucha. "Recurrent Convolutional Structures for Audio Spoof and Video Deepfake Detection." IEEE Journal of Selected Topics in Signal Processing 14, no. 5 (August 2020): 1024–37. http://dx.doi.org/10.1109/jstsp.2020.2999185.

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Arti, Yuni, and Aniati Murni Arymurthy. "Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal." Jurnal Ilmu Komputer dan Informasi 16, no. 1 (March 1, 2023): 47–57. http://dx.doi.org/10.21609/jiki.v16i1.1100.

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Face spoofing can provide inaccurate face verification results in the face recognition system. Deep learning has been widely used to solve face spoofing problems. In face spoofing detection, it is unnecessary to use the entire network layer to represent the difference between real and spoof features. This study detects face spoofing by cutting the Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results showed that face spoofing detection has a good performance on the RGB and fusion models. Both models have better performance than the depth model because RGB modal can represent the difference between real and spoof features, and RGB modal dominate the fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset.

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Rahman, Md Mustafejur, Md Mustafizur Rahman, Saif Ibne Reza, Sumonto Sarker, and Md Mehedi Islam. "Proposed an Algorithm for Preventing IP Spoofing DoS Attack on Neighbor Discovery Protocol of IPv6 in Link Local Network." European Journal of Engineering Research and Science 4, no. 12 (December 17, 2019): 65–70. http://dx.doi.org/10.24018/ejers.2019.4.12.1644.

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Duplicate Address Detection (DAD) is one of the most interesting features in IPv6. It allows nodes to connect to a network by generating a unique IP address. It works on two Neighbor Discovery (ND) messages, namely, Neighbor Solicitation (NS) and Neighbor Advertisement (NA). To verify the uniqueness of generating IP, it sends that IP address via NS message to existing hosts. Any malicious node can receive NS message and can send a spoof reply, thereby initiates a DoS attack and prevents auto configuration process. In this manner, DAD is vulnerable to such DoS attack. This study aims to prevent those malicious nodes from sending spoof reply by securing both NS and NA messages. The proposed Advanced Bits Security (ABS) technique is based on Blake2 algorithm and introducing a creative option called ABS field that holds the hash value of tentative IP address and attached to both NA and NS message. We expect the ABS technique can prevent spoof reply during DAD procedure in link local network and can prevent DoS attack

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Rahman, Md Mustafejur, Md Mustafizur Rahman, Saif Ibne Reza, Sumonto Sarker, and Md Mehedi Islam. "Proposed an Algorithm for Preventing IP Spoofing DoS Attack on Neighbor Discovery Protocol of IPv6 in Link Local Network." European Journal of Engineering and Technology Research 4, no. 12 (December 17, 2019): 65–70. http://dx.doi.org/10.24018/ejeng.2019.4.12.1644.

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Duplicate Address Detection (DAD) is one of the most interesting features in IPv6. It allows nodes to connect to a network by generating a unique IP address. It works on two Neighbor Discovery (ND) messages, namely, Neighbor Solicitation (NS) and Neighbor Advertisement (NA). To verify the uniqueness of generating IP, it sends that IP address via NS message to existing hosts. Any malicious node can receive NS message and can send a spoof reply, thereby initiates a DoS attack and prevents auto configuration process. In this manner, DAD is vulnerable to such DoS attack. This study aims to prevent those malicious nodes from sending spoof reply by securing both NS and NA messages. The proposed Advanced Bits Security (ABS) technique is based on Blake2 algorithm and introducing a creative option called ABS field that holds the hash value of tentative IP address and attached to both NA and NS message. We expect the ABS technique can prevent spoof reply during DAD procedure in link local network and can prevent DoS attack

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Anand, Diksha, and Kamal Gupta. "Face Spoof Detection System Based on Genetic Algorithm and Artificial Intelligence Technique: A Review." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 51. http://dx.doi.org/10.23956/ijarcsse.v8i6.722.

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Face recognition is an alternative means to authenticate a person in different applications for access control. Instead of many improvements, this method is prone to various attacks like photos, 3D masks and video replay attack. Due to these attacks, system should require a face spoof detection system. A face spoof detection systems have an ability to identify whether a face is from a real person or a fake image. Face spoofing effect the image by adding deformation in it and also degrades the image pattern quality. Face spoofing detection system automatically identifies the human face is a true face or a fake face. In today's era, face recognition method is widely used to authenticate the face (like for unlocking mobile phones etc.) and providing access to the services or facilities but some intruders use various trick to crack the authentication system by presenting the false face in front of the authentication system, so it become necessity to prevent our face authentication system from face spoofing attack. So the choice of the technique to detect the face spoofing attack should be accurate and highly efficient.

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Agarwal, Shivang, Ajita Rattani, and C. Ravindranath Chowdary. "A-iLearn: An adaptive incremental learning model for spoof fingerprint detection." Machine Learning with Applications 7 (March 2022): 100210. http://dx.doi.org/10.1016/j.mlwa.2021.100210.

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Jia, Xiaofei, Xin Yang, Kai Cao, Yali Zang, Ning Zhang, Ruwei Dai, Xinzhong Zhu, and Jie Tian. "Multi-scale local binary pattern with filters for spoof fingerprint detection." Information Sciences 268 (June 2014): 91–102. http://dx.doi.org/10.1016/j.ins.2013.06.041.

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Shiaeles, S. N., and M. Papadaki. "FHSD: An Improved IP Spoof Detection Method for Web DDoS Attacks." Computer Journal 58, no. 4 (February 21, 2014): 892–903. http://dx.doi.org/10.1093/comjnl/bxu007.

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Papini, Davide. "Lightweight MAC-spoof detection exploiting received signal power and median filtering." International Journal of Critical Computer-Based Systems 3, no. 4 (2012): 247. http://dx.doi.org/10.1504/ijccbs.2012.053204.

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Chadha, Ankita, Azween Abdullah, and Lorita Angeline. "An improved normalized gain-based score normalization technique for spoof detection algorithm." International journal of electrical and computer engineering systems 13, no. 6 (September 1, 2022): 457–65. http://dx.doi.org/10.32985/ijeces.13.6.5.

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A spoof detection algorithm supports the speaker verification system to examine the false claims by an imposter through careful analysis of input test speech. The scores are employed to categorize the genuine and spoofed samples effectively. Under the mismatch conditions, the false acceptance ratio increases and can be reduced by appropriate score normalization techniques. In this article, we are using the normalized Discounted Cumulative Gain (nDCG) norm derived from ranking the speaker’s log-likelihood scores. The proposed scoring technique smoothens the decaying process due to logarithm with an added advantage from the ranking. The baseline spoof detection system employs Constant Q-Cepstral Co-efficient (CQCC) as the base features with a Gaussian Mixture Model (GMM) based classifier. The scores are computed using the ASVspoof 2019 dataset for normalized and without normalization conditions. The baseline techniques including the Zero normalization (Z-norm) and Test normalization (T-norm) are also considered. The proposed technique is found to perform better in terms of improved Equal Error Rate (EER) of 0.35 as against 0.43 for baseline system (no normalization) wrt to synthetic attacks using development data. Similarly, improvements are seen in the case of replay attack with EER of 7.83 for nDCG-norm and 9.87 with no normalization (no-norm). Furthermore, the tandem-Detection Cost Function (t-DCF) scores for synthetic attack are 0.015 for no-norm and 0.010 for proposed normalization. Additionally, for the replay attack the t-DCF scores are 0.195 for no-norm and 0.17 proposed normalization. The system performance is satisfactory when evaluated using evaluation data with EER of 8.96 for nDCG-norm as against 9.57 with no-norm for synthetic attacks while the EER of 9.79 for nDCG-norm as against 11.04 with no-norm for replay attacks. Supporting the EER, the t-DCF for nDCG-norm is 0.1989 and for no-norm is 0.2636 for synthetic attacks; while in case of replay attacks, the t-DCF is 0.2284 for the nDCG-norm and 0.2454 for no-norm. The proposed scoring technique is found to increase spoof detection accuracy and overall accuracy of speaker verification system.

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