Relevant bibliographies by topics / Spoof Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spoof Detection'

Author: Grafiati
Published: 14 March 2023
Last updated: 25 July 2025

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Journal articles on the topic "Spoof Detection"

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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> &nbsp; <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&rsquo;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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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 spoo
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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
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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
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Books on the topic "Spoof Detection"

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Perry, Anne. Long Spoon Lane. Large Print Press, 2005.

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McDermid, Val. Een spoor van bloed. Sijthoff, 2010.

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

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

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

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Pronzini, Bill. Spook: A Nameless Detective Novel. Carroll & Graf Publishers, 2002.

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Pronzini, Bill. Spook: A "Nameless Detective" Novel. Thorndike Press, 2003.

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Pronzini, Bill. Spook: A ""Nameless Detective"" Novel (Nameless Detective Mysteries). BBC Audiobooks America, 2003.

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(Narrator), Nick Sullivan, ed. Spook: A "Nameless Detective" Novel (Nameless Detective Mysteries). BBC Audiobooks America, 2003.

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East, Rorry. He Fell for Her . 38's : (Detective Spoof). Outskirts Press, Incorporated, 2013.

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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. 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_25.

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Singh, Ravi Pratap, Ratnakar Dash, and Ramesh Kumar Mohapatra. "Face Spoof Detection: The Preprocessing Paradigm." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84062-3_2.

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Kushal, Rajeev Sharma, Manish Kumar, and S. P. S. Chauhan. "Face anti-spoof detection technique: A review." In Artificial Intelligence and Information Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003510833-59.

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Guo, Fanglu, and Tzi-cker Chiueh. "Sequence Number-Based MAC Address Spoof Detection." In Lecture Notes in Computer Science. 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4673-4_50.

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Rakesh, K., B. Sumer Singh, A. Surya, and R. Kaladevi. "Image recognition based attendance system with spoof detection." In Artificial Intelligence, Blockchain, Computing and Security Volume 2. CRC Press, 2023. http://dx.doi.org/10.1201/9781032684994-76.

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

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Hegde, Nagaratna P., V. Sireesha, Sriperambuduri Vinay Kumar, Paleti Navya Sri, and Pati Sri Sai Mahitha. "Face Spoof Detection Using Effective Machine Learning Techniques." In Cognitive Science and Technology. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9266-5_44.

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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. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_109.

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Conference papers on the topic "Spoof Detection"

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Dang, Xiaochao, Zhengzhang Zhao, and Ning Wu. "Research on Speech Playback Spoof Detection Based on ASV Spoof 2021." In 2024 International Conference on New Trends in Computational Intelligence (NTCI). IEEE, 2024. https://doi.org/10.1109/ntci64025.2024.10776128.

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Aye Aung, Myat Aye, Hay Mar Soe Naing, Aye Mya Hlaing, Win Pa Pa, Kasorn Galajit, and Candy Olivia Mawalim. "Analysis of Pathological Features for Spoof Detection." In 2024 27th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA). IEEE, 2024. https://doi.org/10.1109/o-cocosda64382.2024.10800195.

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Ma, Fei, Yumeng Yang, Bo Wang, Hongsheng Chen, and Fei Gao. "Microwave Polarization Detection System Based on Spoof Localized Surface Plasmons." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10670837.

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Chakravarty, Nidhi, and Mohit Dua. "Spectrogram Features and Xception Classifier Based Audio Spoof Detection System." In 2024 International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3). IEEE, 2024. https://doi.org/10.1109/ic2e362166.2024.10827392.

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Tahmina, Sayeda, Musfequa Rahman, Syed Md Minhaz Hossain, and Kaushik Deb. "CounterfeitFace: A Convolution Neural Network Approach to Face Spoof Detection." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022069.

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Hussein, Layth, D. Sharanya, Gotte Ranjith Kumar, Prashanth V, and Ramya R. "An Enhanced Face Spoof Detection using ResNet50 Based Cosine Learning Rate." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939883.

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Leepagorn, Puntika, Khaing Zar Mon, Kasorn Galajit, Jaya Shree Hada, Navod Neranjan Thilakarathne, and Jessada Karnjana. "ThaiSpoof : An extension to current methods and database catering advanced spoof detection." In 2024 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2024. https://doi.org/10.1109/isai-nlp64410.2024.10799368.

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Manandhar, Dinesh. "Detection of GNSS Spoof Signals by Multiple Peak Analysis in Signal Acquisition." In IAF Space Communications and Navigation Symposium, Held at the 75th International Astronautical Congress (IAC 2024). International Astronautical Federation (IAF), 2024. https://doi.org/10.52202/078363-0073.

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Huang, Sung-Feng, Heng-Cheng Kuo, Zhehuai Chen, et al. "Detecting the Undetectable: Assessing the Efficacy of Current Spoof Detection Methods Against Seamless Speech Edits." In 2024 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2024. https://doi.org/10.1109/slt61566.2024.10832200.

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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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Reports on the topic "Spoof Detection"

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Alexander, Chris, and Chantz Denowh. PR-652-195104-R01 Development of Heavy Wall ILI Test Samples. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011680.

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Guidance is needed for the pipeline industry's in-line inspection (ILI) technologies as current industry practices address mainly thin-wall pipe specifications as well as spool and defect design. Heavy-wall pipe is mostly found in subsea applications where the predominant threats are internal; however, external defects cannot be discounted. Nondestructive evaluation (NDE) sensing technologies for heavy-wall pipe often have very different specs for examining external versus internal features. The end uses for ILI technologies in the heavy-wall subsea applications often put a premium on low limi
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Denowh, Chantz, Chris Alexander, and Ahmed Hassanin. PR-652-195104-R02 Development of Heavy Wall ILI Test Samples. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012096.

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Guidance is needed for the pipeline industry's in-line inspection (ILI) technologies as current industry practices address mainly thin-wall pipe specifications as well as spool and defect design. Heavy-wall pipe is mostly found in subsea applications where the predominant threats are internal; however, external defects cannot be discounted. Nondestructive evaluation (NDE) sensing technologies for heavy-wall pipe often have very different specifications for examining external versus internal defects. The end uses for ILI technologies in the heavy-wall subsea applications often put a premium on
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