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Journal articles on the topic 'Spoof Detection'

Author: Grafiati

Published: 14 March 2023

Last updated: 25 July 2025

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1

Guo, Jinlin, Yancheng Zhao, and Haoran Wang. "Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing." Applied Sciences 13, no. 13 (2023): 7773. http://dx.doi.org/10.3390/app13137773.

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Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a sy

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Ms., Dilna e. p1, Maneesha Manoj2 Ms., Jiji c. j3 Ms., Jeena c. j. Ms., and Hrudhya k. p5 Ms. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research 4, no. 1 (2017): 40–48. https://doi.org/10.5281/zenodo.10774726.

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<strong>Abstract: </strong>   <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose a

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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 (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 th

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Ms., Dilna e. p1 Ms. Maneesha Manoj2 Ms. Jiji c. j3 Ms. Jeena c. j. 4. Ms. Hrudhya k. p5. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research, ISSN: 2349 –3607 (Online) , ISSN: 2349 –4824 (Print) Vol.4,, Issue 1, Jan-2017, (2017): pp 40–48. https://doi.org/10.5281/zenodo.242479.

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<strong>Abstract: </strong> <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient a

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Kim, Seung Gu, Jin Seong Hong, Jung Soo Kim, and Kang Ryoung Park. "Estimation of Fractal Dimension and Detection of Fake Finger-Vein Images for Finger-Vein Recognition." Fractal and Fractional 8, no. 11 (2024): 646. http://dx.doi.org/10.3390/fractalfract8110646.

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With recent advancements in deep learning, spoofing techniques have developed and generative adversarial networks (GANs) have become an emerging threat to finger-vein recognition systems. Therefore, previous research has been performed to generate finger-vein images for training spoof detectors. However, these are limited and researchers still cannot generate elaborate fake finger-vein images. Therefore, we develop a new densely updated contrastive learning-based self-attention generative adversarial network (DCS-GAN) to create elaborate fake finger-vein images, enabling the training of corres

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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 (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 in

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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 (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 s

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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 (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 u

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Pal, Lovely, and Renuka Singh. "A Face Spoof Detection using Feature Extraction and SVM." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 629–34. http://dx.doi.org/10.21275/sr221103150851.

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Gada, Amay, Neel Kothari, Ruhina Karani, Chetashri Badane, Dhruv Gada, and Tanish Patwa. "DR-SASV: A deep and reliable spoof aware speech verification system." International Journal on Information Technologies and Security 15, no. 4 (2023): 93–106. http://dx.doi.org/10.59035/ffmb8272.

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A spoof-aware speaker verification system is an integrated system that is capable of jointly identifying impostor speakers as well as spoofing attacks from target speakers. This type of system largely helps in protecting sensitive data, mitigating fraud, and reducing theft. Research has recently enhanced the effectiveness of countermeasure systems and automatic speaker verification systems separately to produce low Equal Error Rates (EER) for each system. However, work exploring a combination of both is still scarce. This paper proposes an end-to-end solution to address spoof-aware automatic s

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Kim, Jung Soo, Young Won Lee, Jin Seong Hong, Seung Gu Kim, Ganbayar Batchuluun, and Kang Ryoung Park. "LRFID-Net: A Local-Region-Based Fake-Iris Detection Network for Fake Iris Images Synthesized by a Generative Adversarial Network." Mathematics 11, no. 19 (2023): 4160. http://dx.doi.org/10.3390/math11194160.

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Iris recognition is a biometric method using the pattern of the iris seated between the pupil and the sclera for recognizing people. It is widely applied in various fields owing to its high accuracy in recognition and high security. A spoof detection method for discriminating a spoof attack is essential in biometric recognition systems that include iris recognition. However, previous studies have mainly investigated spoofing attack detection methods based on printed or photographed images, video replaying, artificial eyes, and patterned contact lenses fabricated using iris images from informat

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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 (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

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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 (2015): 83–94. http://dx.doi.org/10.14257/ijsia.2015.9.10.07.

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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 (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. Therefor

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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 (2018): 2859–63. https://doi.org/10.5281/zenodo.1257853.

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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. Ther

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Prasad, Mayank, Sandhya Jain, Praveen Bhanodia, and Anu Priya. "Influence of Standalone and Ensemble Classifiers in Face Spoofing Detection using LBP and CNN Models." European Journal of Electrical Engineering and Computer Science 8, no. 2 (2024): 17–30. http://dx.doi.org/10.24018/ejece.2024.8.2.604.

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Background: Artificial intelligence has made significant contributions to facial recognition and biometric identification and is now being employed in a range of applications. Detecting facial spoofing, where someone attempts to pass as an authorized user to gain access to the system, is still difficult. Spoofing-attack-resistant face recognition systems demand efficient and effective solutions. A more stringent recognition system will result in higher false positives and false negatives, which makes such a system questionable for practical use. Eventually, the prominent deep-learning techniqu

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Li, Kaicheng, Hongyu Yang, Binghui Chen, Pengyu Li, Biao Wang, and Di Huang. "Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1351–59. http://dx.doi.org/10.1609/aaai.v37i1.25219.

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Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof t

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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 (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 s

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Suman, Saurabh, and Dr Nagesh Salimath. "Designing Intuitive and Effective Dynamic Facial Authentication: Machine Interaction with Human Factors." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1642–46. http://dx.doi.org/10.22214/ijraset.2023.54946.

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Abstract: The objective of this paper is to propose a spoof-free Face Liveness Detection system with an active approach that uses the challenge-response methodology which detects the motions and gestures of the user, thereby analyzing and recognizing a real face from a photo. With cybercrimes on the rise each day, identity thefts being one of them- especially in an unsupervised authentication system, has given rise to serious security concerns. Face liveness detection assures the user's actual presence and validates their identities. Along with that, it prevents fraudulent reproduction of face

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Tamtama, Gabriel Indra Widi, and I. Kadek Dendy Senapartha. "Fake Face Detection System Using MobileNets Architecture." CESS (Journal of Computer Engineering, System and Science) 8, no. 2 (2023): 329. http://dx.doi.org/10.24114/cess.v8i2.43762.

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Sistem pengenalan wajah merupakan salah satu metode dalam teknik biometric yang menggunakan wajah untuk proses identifikasi atau verifikasi seseorang. Teknologi ini tidak memerlukan kontak fisik seperti verifikasi sidik jari dan diklaim lebih aman karena wajah setiap orang memiliki karakter yang berbeda-beda. Terdapat dua fase utama dalam sistem biometrik wajah, yaitu deteksi wajah palsu Presentation Attack (PA) detektor dan pengenalan wajah (face recognition). Penelitian ini melakukan eksperimen dengan tujuan membangun sebuah model pembelajaran mesin (machine learning) berbasis mobile untuk m

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Kaur, Ramandeep, and P. S. "Techniques of Face Spoof Detection: A Review." International Journal of Computer Applications 164, no. 1 (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 (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 (2015): 746–61. http://dx.doi.org/10.1109/tifs.2015.2400395.

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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 (2016): 2268–83. http://dx.doi.org/10.1109/tifs.2016.2578288.

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

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Muradkhanli, Leyla, and Parviz Namazli. "FACE SPOOF DETECTION USING CONVOLUTIONAL NEURAL NETWORK." Problems of Information Society 14, no. 2 (2023): 40–46. http://dx.doi.org/10.25045/jpis.v14.i2.05.

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The paper suggests a technique that uses convolutional neural network (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fake facial images. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, a standardized data

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Akash, Chaudhary, AnkitaSingh, and Km.Yachana. "Anti Spoofing Face Detection with Convolutional Neural Networks Classifier." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 745–50. https://doi.org/10.5281/zenodo.7953326.

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The ability to detect spoofed faces has become a critical concern in various applications, such as face recognition systems, banking, and security measures. Thisresearchpresentsa simple system that can detect whether a facein video stream is spoofed or real using pre-trained models for face detection and anti-spoofing. The system uses a continuous loop to read each frame of the video stream, to assess whether a face image is real or spoof, first detect faces using the pre-trained face detection model, then crop and resize the face image. If the model predicts that the face is fake, the system

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Dave, Vani. "Spoof Detection Using Local Binary Pattern In Face." Jurnal Ilmu Komputer 13, no. 1 (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 phot

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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 (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

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Dişken, Gökay, and Zekeriya Tüfekci. "Noise-Robust Spoofed Speech Detection Using Discriminative Autoencoder." Celal Bayar Üniversitesi Fen Bilimleri Dergisi 19, no. 2 (2023): 167–74. https://doi.org/10.18466/cbayarfbe.1132319.

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Audio spoof detection gained attention of the researchers recently, as it is vital to detect spoofed speech for automatic speaker recognition systems. Publicly available datasets also accelerated the studies in this area. Many different features and classifiers have been proposed to overcome the spoofed speech detection problem, and some of them achieved considerably high performances. However, under additive noise, the spoof detection performance drops rapidly. On the other hand, number of studies about robust spoofed speech detection is very limited. The problem becomes more interesting as t

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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 (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 fair

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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

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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 (2021): 14693–721. http://dx.doi.org/10.1007/s11042-020-10338-1.

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Ravindran, Swathika, and K. Geetha. "An Overview of Spoof Detection in ASV Systems." ECS Transactions 107, no. 1 (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 stres

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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 (2015): 373–84. http://dx.doi.org/10.14257/ijsip.2015.8.8.38.

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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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Hassani, Ali, Jon Diedrich, and Hafiz Malik. "Monocular Facial Presentation–Attack–Detection: Classifying Near-Infrared Reflectance Patterns." Applied Sciences 13, no. 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 vari

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Huszár, Viktor Dénes, and Vamsi Kiran Adhikarla. "Live Spoofing Detection for Automatic Human Activity Recognition Applications." Sensors 21, no. 21 (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 surveillan

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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 (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, m

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Parveen, Dr Sajida, Sharifah Mumtazah Syed Ahmad, Nadeem Naeem Bhatti, Imtiaz Ali Halepoto, and Shamashad Lakho. "Robust Hybrid Texture Descriptor (HTD) and a parallel score based fusion for face verification and liveness detection system." VFAST Transactions on Software Engineering 12, no. 2 (2024): 85–94. http://dx.doi.org/10.21015/vtse.v12i2.1828.

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Currently, most of the biometric recognition systems are based on face verification are susceptible to the spoof attacks. Video replays, printed photographs and 3D mask attacks provoke false acceptance lest some necessary counter-measures are employed. We focus on still face spoof attacks considered as one of the most easily generated attacks and challenging for modern face verification systems. To detect face spoofing, most of existing countermeasures focus on designing discriminative features to analyze the textural properties of facial skin. To improve the texture discriminating properties

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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 (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 b

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Cheniti, Mohamed, Zahid Akhtar, and Praveen Kumar Chandaliya. "Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50." Journal of Imaging 11, no. 2 (2025): 42. https://doi.org/10.3390/jimaging11020042.

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In this paper, we address the challenge of fingerprint liveness detection by proposing a dual pre-trained model approach that combines VGG16 and ResNet50 architectures. While existing methods often rely on a single feature extraction model, they may struggle with generalization across diverse spoofing materials and sensor types. To overcome this limitation, our approach leverages the high-resolution feature extraction of VGG16 and the deep layer architecture of ResNet50 to capture a more comprehensive range of features for improved spoof detection. The proposed approach integrates these two mo

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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 (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 (G

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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 (2015): 2447–60. http://dx.doi.org/10.1109/tifs.2015.2464772.

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

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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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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 (2013): 260–61. http://dx.doi.org/10.1049/el.2012.4173.

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