Relevant bibliographies by topics / Face spoofing detection

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

Academic literature on the topic 'Face spoofing detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Face spoofing detection"

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Lai, Zhimao, Yang Guo, Yongjian Hu, Wenkang Su, and Renhai Feng. "Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach." Sensors 24, no. 24 (2024): 8075. https://doi.org/10.3390/s24248075.

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Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces. If these instances are incorrectly flagged as fraudulent by face anti-spoofing systems, it could lead to user inconvenience. In response, we develop a face anti-spoofing database that includes light makeup faces and establishes a criterion for determining light makeup to select appropriate data. Building on this foundation, we assess multiple established face anti-spoofing algorithms using the newly created database. Our findings reveal that the majority of these algorithms experience a decrease in performance when faced with light makeup faces. Consequently, this paper introduces a general face anti-spoofing algorithm specifically designed for light makeup faces, which includes a makeup augmentation module, a batch channel normalization module, a backbone network updated via the Exponential Moving Average (EMA) method, an asymmetric virtual triplet loss module, and a nearest neighbor supervised contrastive module. The experimental outcomes confirm that the proposed algorithm exhibits superior detection capabilities when handling light makeup faces.
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Journal, IJSREM. "Anti Face Spoofing System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–12. http://dx.doi.org/10.55041/ijsrem37068.

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Face spoofing, the act of using deceptive techniques such as printed photos or digital images to deceive facial recognition systems, poses a significant threat to the security of various applications, including biometric authentication and access control systems. This paper presents a concise yet effective approach to address the challenge of anti-face spoofing using Python within a limited codebase of 50 lines. The proposed solution leverages a combination of image processing techniques and machine learning algorithms to detect and prevent face spoofing attempts. A pre-trained deep neural network model for facial recognition is employed to extract essential facial features. Subsequently, the system utilizes image manipulation detection methods to identify anomalies indicative of face spoofing attacks. The implementation showcases the simplicity and efficiency of the proposed anti-face spoofing technique, demonstrating the potential for integration into real- world applications with minimal computational overhead. The concise Python code enables easy adoption and adaptation for developers aiming to enhance the security of facial recognition systems against face spoofing threats. The experimental results demonstrate the effectiveness of the approach in accurately distinguishing between genuine and spoofed facial images, thereby contributing to the robustness of facial recognition systems in the presence of adversarial attacks. Keywords - Liveness Detection, Presentation Attack Detection, Spoof Detection Algorithms, Biometric Security, Face Recognition Robustness, 3D Face Modeling, Texture Analysis, Deep Learning Anti- Spoofing, Multi-Spectral Imaging, Behavioral Biometrics, Challenge-Response Mechanisms, Optical Flow Analysis, Pulse Detection, Surface Reflectance, Depth Sensing, Motion Analysis, Pattern Recognition, Anti-Spoofing Networks, Temporal Feature Extraction, Anomaly Detection.
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M, Parvadhi, Kishore S, Narayana Sakthivel S, Lalith Kumar K, and Prasana Kumar K. S. "ENHANCED FACE SPOOFING DETECTION THROUGH GEOMETRIC TEMPORAL DYNAMIC ANALYSIS." International Journal of Technical Research & Science 9, Spl (2024): 27–35. http://dx.doi.org/10.30780/specialissue-iset-2024/012.

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Face spoofing, the act of presenting a fake face or biometric feature to deceive authentication systems, poses a significant threat to the security of facial recognition systems. With the proliferation of biometric authentication in various applications, including mobile devices, banking, and surveillance systems, the vulnerability to face spoofing attacks has become a pressing concern. This paper provides a comprehensive review and analysis of face spoofing detection techniques, focusing on both traditional methods and recent advancements. The review begins by outlining the various types of face spoofing attacks, including printed photos, replay attacks, 3D masks, and makeup disguises. Subsequently, it discusses the challenges faced by face spoofing detection systems, such as the high variability in spoofing materials, illumination conditions, and presentation attacks. Traditional techniques, including texture analysis, motion analysis, and color-based methods, are examined, highlighting their strengths and limitations Furthermore, the paper explores recent advancements in face anti-spoofing, including deep learning-based approaches, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). It discusses the effectiveness of these techniques in mitigating the vulnerabilities of conventional methods and their ability to handle complex spoofing attacks with higher accuracy and robustness. Additionally, the review investigates datasets commonly used for training and evaluating face spoofing detection algorithms, emphasizing the importance of diverse and representative datasets for reliable performance assessment. Furthermore, it discusses evaluation metrics, benchmarking protocols, and open challenges in the field to provide insights into future research directions.
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Setyawan, Marselinus Putu Harry, I. Dewa Made Bayu Atmaja Darmawan, I. Gede Santi Astawa, and I. Wayan Santiyasa. "Bahasa Indonesia." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 13, no. 1 (2024): 179. https://doi.org/10.24843/jlk.2024.v13.i01.p17.

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This research aims to develop a spoofing detection system using the Support Vector Machine (SVM) method with texture feature extraction of Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM). The system is intended to address the challenge of student attendance by integrating face recognition technology into attendance management. Spoofing, which is an attempt to counterfeit faces, poses a challenge in face-based security systems. Therefore, the system focuses on spoofing detection by comparing texture patterns between real and fake faces. The model is able to identify face spoofing attempts with an accuracy rate of 94% after tuning the C and gamma parameters. Furthermore, the anti-spoofing attendance system is tested through black box testing and provides results that meet expectations. The system is able to start classes, record student attendance, and generate valid attendance reports. The entire system functions have been thoroughly tested and achieve a 95% accuracy rate in spoofing detection.
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Su-Gyeong Yu, Su-Gyeong Yu, So-Eui Kim Su-Gyeong Yu, Kun Ha Suh So-Eui Kim, and Eui Chul Lee Kun Ha Suh. "Effect of Facial Shape Information Reflected on Learned Features in Face Spoofing Detection." 網際網路技術學刊 23, no. 3 (2022): 517–25. http://dx.doi.org/10.53106/160792642022052303010.

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<p>Face recognition is a convenient and non-contact biometric method used widely for secure personal authentication. However, the face is an exposed body part, and face spoofing attacks, which compromise the security of systems that use face recognition for authentication, are frequently reported. Previous face spoofing attack detection studies proposed texture-analysis-based methods using handcrafted features or learned features to prevent spoofing attacks. However, it is unclear whether spoofing attack images reflect the face distortion resulting from failing to reflect the three-dimensional structure of a real face. To resolve this problem, we compared and analyzed the face spoofing attack detection performances of two typical convolutional neural network models, namely ResNet-18 and DenseNet-121. CASIA-FASD, Replay-Attack, and PR-FSAD were used as the training data. The classification performance of the model was evaluated based on four protocols. DenseNet-121 exhibited better performance in most scenarios. DenseNet-121 reflected facial shape information well by uniformly applying the learned features of both the initial and final layers during training. It is expected that this study will support the realization of spoofing technology with enhanced security.</p> <p> </p>
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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 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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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 draws a red rectangle around the face and displays the label "spoof." If the model predicts that the face is real, the system draws a green rectangle around the face and displays the label "real." The proposed system achieved a high accuracy rate in detecting spoofed faces, making it suitable for real-world applications.
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Zahra, Sayyam, Mohibullah Khan, Kamran Abid, Naeem Aslam, and Ejaz Ahmad Khera. "A Novel Face Spoofing Detection Using hand crafted MobileNet." VFAST Transactions on Software Engineering 11, no. 2 (2023): 34–42. http://dx.doi.org/10.21015/vtse.v11i2.1485.

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There are several uses for face spoofing detection, including human-robot communication, business, film, hotel services, and even politics. Despite the adoption of numerous supervised and unsupervised techniques in a wide range of domains, proper analysis is still lacking. As a result, we chose this difficulty as our study problem. We have put out a method for the effective and precise classification of face spoofing that may be used for a variety of everyday issues. This work attempts to investigate the ideal method and parameters to offer a solution for a powerful deep learning spoofing detection system. In this study, we used the LCC FASD dataset and deep learning algorithms to recognize faces from photos. Precision and accuracy are used as the evaluation measures to assess the performance of the CNN (Convolutional Neural Network) model. The results of the studies demonstrate that the model was effective at spoofing face picture detection. The accuracy of the CNN model was 0.98. Overall, the study's findings show that spoofing detection from photos using the LCC FASD dataset can be successfully performed utilizing deep learning algorithms. Yet, the findings of this study offer a strong framework for further investigation in this area.
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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 (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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Sun, Xudong, Lei Huang, and Changping Liu. "Multispectral face spoofing detection using VIS–NIR imaging correlation." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (2018): 1840003. http://dx.doi.org/10.1142/s0219691318400039.

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With the wide applications of face recognition techniques, spoofing detection is playing an important role in the security systems and has drawn much attention. This research presents a multispectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging, which exploits VIS–NIR image consistency for spoofing detection. First, we use part-based methods to extract illumination robust local descriptors, and then the consistency is calculated to perform spoofing detection. In order to further exploit multispectral correlation in local patches and to be free from manually chosen regions, we learn a confidence factor map for all the patches, which is used in final classifier. Experimental results of self-collected datasets, public Msspoof and PolyU-HSFD datasets show that the proposed approach gains promising results for both intra-dataset and cross-dataset testing scenarios, and that our method can deal with different illumination and both photo and screen spoofing.
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Dissertations / Theses on the topic "Face spoofing detection"

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Abd, Aziz Azim Zaliha Binti. "Vision-based spoofing face detection using polarised light." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/75434/.

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Computer vision is an image understanding discipline that studies how to reconstruct, interpret and understand a 3D scene from its 2D images. One of the goals is to automate the analysis of images through the use of computer software and hardware. Meanwhile, biometrics refer to the automated authentication process that rely on measureable physical characteristics such as individual’s unique fingerprints, iris, face, palmprint, gait and voice. Amongst these biometric identification schemes, face biometric is said to be the most popular where face authentication systems have been rapidly developed mainly for security reasons. However, the resistance of face biometric system to spoofing attack, which is an act to impersonate a valid user by placing fake face in front of the sensor to gain access, has become a critical issue. Thus, anti-spoofing technique is required to counter the attacks. Different materials have their own reflection properties. These reflection differences have been manipulated by researches for particular reasons such as in object classification. Many ways can be used to measure the reflection differences of each object. One of them is by using polarised light. Since none of the existing studies applied polarised light in face spoofing detection, therefore in this thesis, polarisation imaging technique was implemented to distinguish between genuine face and two types of spoofing attacks: printed photos and iPad displayed faces. From the investigations, several research findings can be listed. Firstly, unpolarised visible light could not be used in a polarisation imaging system to capture polarised images for designated purpose. Secondly, polarised light is able to differentiate between surface and subsurface reflections of real and fake faces. However, both of these reflections could not be used as one of the classification methods between real face and printed photos. Thirdly, polarised image could contribute to enhance the performance of face recognition system against spoofing attacks in which the newly proposed formula, SDOLP3F achieves higher accuracy rate. Next, near infrared (NIR) light in a polarisation imaging system do not provide significant differences between real face and the two face attacks. Apart from polarised spoofing face detection analysis, experiments to investigate the accuracy of depth data captured by three depth sensors was carried out. This investigation was conducted due to the concerns over the stability of the depth pixels involved in 3D spoofing face reconstruction in a publicly available spoofing face database known as 3DMAD. From the analysis, none of the three depth sensors which are the Kinect for Xbox 360, Kinect for Windows version 2.0 and Asus Xtion Pro Live are suitable for 3D face reconstruction for the purpose of spoofing detection due to the potential errors made by the fluctuated pixels. As a conclusion, polarisation imaging technique has the potential to protect face biometric system from printed photos and iPad displayed attacks. Further investigations using the same polarised light approach could be carried out on other future work as proposed at the end of this thesis.
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Boulkenafet, Z. (Zinelabidine). "Face presentation attack detection using texture analysis." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526219257.

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Abstract In the last decades, face recognition systems have evolved a lot in terms of performance. As a result, this technology is now considered as mature and is applied in many real world applications from border control to financial transactions and computer security. Yet, many studies show that these systems suffer from vulnerabilities to spoofing attacks, a weakness that may limit their usage in many cases. A face spoofing attack or presentation attack occurs when someone tries to masquerade as someone else by presenting a fake face in front of the face recognition camera. To protect the recognition systems against attacks of this kind, many face anti-spoofing methods have been proposed. These methods have shown good performances on the existing face anti-spoofing databases. However, their performances degrade drastically under real world variations (e.g., illumination and camera device variations). In this thesis, we concentrate on improving the generalization capabilities of the face anti-spoofing methods with a particular focus on the texture based techniques. In contrast to most existing texture based methods aiming at extracting texture features from gray-scale images, we propose a joint color-texture analysis. First, the face images are converted into different color spaces. Then, the feature histograms computed over each image band are concatenated and used for discriminating between real and fake face images. Our experiments conducted on three color spaces: RGB, HSV and YCbCr show that extracting the texture information from separated luminance chrominance color spaces (HSV and YCbCr) yields to better performances compared to gray-scale and RGB image representations. Moreover, to deal with the problem of illumination and image-resolution variations, we propose to extract this texture information from different scale images. In addition to representing the face images in different scales, the multi-scale filtering methods also act as pre-processing against factors such as noise and illumination. Although our obtained results are better than the state of the art, they are still far from the requirements of real world applications. Thus, to help in the development of robust face anti-spoofing methods, we collected a new challenging face anti-spoofing database using six camera devices in three different illumination and environmental conditions. Furthermore, we have organized a competition on the collected database where fourteen face anti-spoofing methods have been assessed and compared<br>Tiivistelmä Kasvontunnistusjärjestelmien suorituskyky on parantunut huomattavasti viime vuosina. Tästä syystä tätä teknologiaa pidetään nykyisin riittävän kypsänä ja käytetään jo useissa käytännön sovelluksissa kuten rajatarkastuksissa, rahansiirroissa ja tietoturvasovelluksissa. Monissa tutkimuksissa on kuitenkin havaittu, että nämä järjestelmät ovat myös haavoittuvia huijausyrityksille, joissa joku yrittää esiintyä jonakin toisena henkilönä esittämällä kameralle jäljennöksen kohdehenkilön kasvoista. Tämä haavoittuvuus rajoittaa kasvontunnistuksen laajempaa käyttöä monissa sovelluksissa. Tunnistusjärjestelmien turvaamiseksi on kehitetty lukuisia menetelmiä tällaisten hyökkäysten torjumiseksi. Nämä menetelmät ovat toimineet hyvin tätä tarkoitusta varten kehitetyillä kasvotietokannoilla, mutta niiden suorituskyky huononee dramaattisesti todellisissa käytännön olosuhteissa, esim. valaistuksen ja käytetyn kuvantamistekniikan variaatioista johtuen. Tässä työssä yritämme parantaa kasvontunnistuksen huijauksen estomenetelmien yleistämiskykyä keskittyen erityisesti tekstuuripohjaisiin menetelmiin. Toisin kuin useimmat olemassa olevat tekstuuripohjaiset menetelmät, joissa tekstuuripiirteitä irrotetaan harmaasävykuvista, ehdotamme väritekstuurianalyysiin pohjautuvaa ratkaisua. Ensin kasvokuvat muutetaan erilaisiin väriavaruuksiin. Sen jälkeen kuvan jokaiselta kanavalta erikseen lasketut piirrehistogrammit yhdistetään ja käytetään erottamaan aidot ja väärät kasvokuvat toisistaan. Kolmeen eri väriavaruuteen, RGB, HSV ja YCbCr, perustuvat testimme osoittavat, että tekstuuri-informaation irrottaminen HSV- ja YCbCr-väriavaruuksien erillisistä luminanssi- ja krominanssikuvista parantaa suorituskykyä kuvien harmaasävy- ja RGB-esitystapoihin verrattuna. Valaistuksen ja kuvaresoluution variaation takia ehdotamme myös tämän tekstuuri-informaation irrottamista eri tavoin skaalatuista kuvista. Sen lisäksi, että itse kasvot esitetään eri skaaloissa, useaan skaalaan perustuvat suodatusmenetelmät toimivat myös esikäsittelynä sellaisia suorituskykyä heikentäviä tekijöitä vastaan kuten kohina ja valaistus. Vaikka tässä tutkimuksessa saavutetut tulokset ovat parempia kuin uusinta tekniikkaa edustavat tulokset, ne ovat kuitenkin vielä riittämättömiä reaalimaailman sovelluksissa tarvittavaan suorituskykyyn. Sen takia edistääksemme uusien robustien kasvontunnistuksen huijaamisen ilmaisumenetelmien kehittämistä kokosimme uuden, haasteellisen huijauksenestotietokannan käyttäen kuutta kameraa kolmessa erilaisessa valaistus- ja ympäristöolosuhteessa. Järjestimme keräämällämme tietokannalla myös kansainvälisen kilpailun, jossa arvioitiin ja verrattiin neljäätoista kasvontunnistuksen huijaamisen ilmaisumenetelmää
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Komulainen, J. (Jukka). "Software-based countermeasures to 2D facial spoofing attacks." Doctoral thesis, Oulun yliopisto, 2015. http://urn.fi/urn:isbn:9789526208732.

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Abstract Because of its natural and non-intrusive interaction, identity verification and recognition using facial information is among the most active areas in computer vision research. Unfortunately, it has been shown that conventional 2D face recognition techniques are vulnerable to spoofing attacks, where a person tries to masquerade as another one by falsifying biometric data and thereby gaining an illegitimate advantage. This thesis explores different directions for software-based face anti-spoofing. The proposed approaches are divided into two categories: first, low-level feature descriptors are applied for describing the static and dynamic characteristic differences between genuine faces and fake ones in general, and second, complementary attack-specific countermeasures are investigated in order to overcome the limitations of generic spoof detection schemes. The static face representation is based on a set of well-known feature descriptors, including local binary patterns, Gabor wavelet features and histogram of oriented gradients. The key idea is to capture the differences in quality, light reflection and shading by analysing the texture and gradient structure of the input face images. The approach is then extended to the spatiotemporal domain when both facial appearance and dynamics are exploited for spoof detection using local binary patterns from three orthogonal planes. It is reasonable to assume that no generic spoof detection scheme is able to detect all known, let alone unseen, attacks scenarios. In order to find out well-generalizing countermeasures, the problem of anti-spoofing is broken into two attack-specific sub-problems based on whether the spoofing medium can be detected in the provided view or not. The spoofing medium detection is performed by describing the discontinuities in the gradient structures around the detected face. If the display medium is concealed outside the view, a combination of face and background motion correlation measurement and texture analysis is applied. Furthermore, an open-source anti-spoofing fusion framework is introduced and its system-level performance is investigated more closely in order to gain insight on how to combine different anti-spoofing modules. The proposed spoof detection schemes are evaluated on the latest benchmark datasets. The main findings of the experiments are discussed in the thesis<br>Tiivistelmä Kasvokuvaan perustuvan henkilöllisyyden tunnistamisen etuja ovat luonnollinen vuorovaikutus ja etätunnistus, minkä takia aihe on ollut erittäin aktiivinen tutkimusalue konenäön tutkimuksessa. Valitettavasti tavanomaiset kasvontunnistustekniikat ovat osoittautuneet haavoittuvaisiksi hyökkäyksille, joissa kameralle esitetään jäljennös kohdehenkilön kasvoista positiivisen tunnistuksen toivossa. Tässä väitöskirjassa tutkitaan erilaisia ohjelmistopohjaisia ratkaisuja keinotekoisten kasvojen ilmaisuun petkuttamisen estämiseksi. Työn ensimmäisessä osassa käytetään erilaisia matalan tason piirteitä kuvaamaan aitojen ja keinotekoisten kasvojen luontaisia staattisia ja dynaamisia eroavaisuuksia. Työn toisessa osassa esitetään toisiaan täydentäviä hyökkäystyyppikohtaisia vastakeinoja, jotta yleispätevien menetelmien puutteet voitaisiin ratkaista ongelmaa rajaamalla. Kasvojen staattisten ominaisuuksien esitys perustuu yleisesti tunnettuihin matalan tason piirteisiin, kuten paikallisiin binäärikuvioihin, Gabor-tekstuureihin ja suunnattujen gradienttien histogrammeihin. Pääajatuksena on kuvata aitojen ja keinotekoisten kasvojen laadun, heijastumisen ja varjostumisen eroavaisuuksia tekstuuria ja gradienttirakenteita analysoimalla. Lähestymistapaa laajennetaan myös tila-aika-avaruuteen, jolloin hyödynnetään samanaikaisesti sekä kasvojen ulkonäköä ja dynamiikkaa irroittamalla paikallisia binäärikuvioita tila-aika-avaruuden kolmelta ortogonaaliselta tasolta. Voidaan olettaa, ettei ole olemassa yksittäistä yleispätevää vastakeinoa, joka kykenee ilmaisemaan jokaisen tunnetun hyökkäystyypin, saati tuntemattoman. Näin ollen työssä keskitytään tarkemmin kahteen hyökkäystilanteeseen. Ensimmäisessä tapauksessa huijausapuvälineen reunoja ilmaistaan analysoimalla gradienttirakenteiden epäjatkuvuuksia havaittujen kasvojen ympäristössä. Jos apuvälineen reunat on piilotettu kameran näkymän ulkopuolelle, petkuttamisen ilmaisu toteutetaan yhdistämällä kasvojen ja taustan liikkeen korrelaation mittausta ja kasvojen tekstuurianalyysiä. Lisäksi työssä esitellään vastakeinojen yhdistämiseen avoimen lähdekoodin ohjelmisto, jonka avulla tutkitaan lähemmin menetelmien fuusion vaikutuksia. Tutkimuksessa esitetyt menetelmät on kokeellisesti vahvistettu alan viimeisimmillä julkisesti saatavilla olevilla tietokannoilla. Tässä väitöskirjassa käydään läpi kokeiden päähavainnot
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Sarkar, Abhijit. "Cardiac Signals: Remote Measurement and Applications." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78739.

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The dissertation investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals. We have mainly discussed two major cardiac signals: electrocardiogram (ECG), and photoplethysmogram (PPG). ECG and PPG signals hold strong potential for biometric authentications and identifications. We have shown that by mapping each cardiac beat from time domain to an angular domain using a limit cycle, intra-class variability can be significantly minimized. This is in contrary to conventional time domain analysis. Our experiments with both ECG and PPG signal shows that the proposed method eliminates the effect of instantaneous heart rate on the shape morphology and improves authentication accuracy. For noninvasive measurement of PPG beats, we have developed a systematic algorithm to extract pulse rate from face video in diverse situations using video magnification. We have extracted signals from skin patches and then used frequency domain correlation to filter out non-cardiac signals. We have developed a novel entropy based method to automatically select skin patches from face. We report beat-to-beat accuracy of remote PPG (rPPG) in comparison to conventional average heart rate. The beat-to-beat accuracy is required for applications related to heart rate variability (HRV) and affective computing. The algorithm has been tested on two datasets, one with static illumination condition and the other with unrestricted ambient illumination condition. Automatic skin detection is an intermediate step for rPPG. Existing methods always depend on color information to detect human skin. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. We have used LBP lacunarity based micro-textures features and a region growing algorithm to find skin pixels in an image. Our experiment shows that the proposed method is applicable universally to any image including near infra-red images. This finding helps to extend the domain of many application including rPPG. To the best of our knowledge, this is first such method that is independent of color cues.<br>Ph. D.
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Tak, Hemlata. "End-to-End Modeling for Speech Spoofing and Deepfake Detection." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS104.pdf.

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Les systèmes biométriques vocaux sont utilisés dans diverses applications pour une authentification sécurisée. Toutefois, ces systèmes sont vulnérables aux attaques par usurpation d'identité. Il est donc nécessaire de disposer de techniques de détection plus robustes. Cette thèse propose de nouvelles techniques de détection fiables et efficaces contre les attaques invisibles. La première contribution est un ensemble non linéaire de classificateurs de sous-bandes utilisant chacun un modèle de mélange gaussien. Des résultats compétitifs montrent que les modèles qui apprennent des indices discriminants spécifiques à la sous-bande peuvent être nettement plus performants que les modèles entraînés sur des signaux à bande complète. Étant donné que les DNN sont plus puissants et peuvent effectuer à la fois l'extraction de caractéristiques et la classification, la deuxième contribution est un modèle RawNet2. Il s'agit d'un modèle de bout en bout qui apprend les caractéristiques directement à partir de la forme d'onde brute. La troisième contribution comprend la première utilisation de réseaux neuronaux graphiques (GNN) avec un mécanisme d'attention pour modéliser la relation complexe entre les indices d'usurpation présents dans les domaines spectral et temporel. Nous proposons un réseau d'attention spectro-temporel E2E appelé RawGAT-ST. Il est ensuite étendu à un réseau d'attention spectro-temporel intégré, appelé AASIST, qui exploite la relation entre les graphes spectraux et temporels hétérogènes. Enfin, cette thèse propose une nouvelle technique d'augmentation des données appelée RawBoost et utilise un modèle vocal auto-supervisé et pré-entraîné pour améliorer la généralisation<br>Voice biometric systems are being used in various applications for secure user authentication using automatic speaker verification technology. However, these systems are vulnerable to spoofing attacks, which have become even more challenging with recent advances in artificial intelligence algorithms. There is hence a need for more robust, and efficient detection techniques. This thesis proposes novel detection algorithms which are designed to perform reliably in the face of the highest-quality attacks. The first contribution is a non-linear ensemble of sub-band classifiers each of which uses a Gaussian mixture model. Competitive results show that models which learn sub-band specific discriminative information can substantially outperform models trained on full-band signals. Given that deep neural networks are more powerful and can perform both feature extraction and classification, the second contribution is a RawNet2 model. It is an end-to-end (E2E) model which learns features directly from raw waveform. The third contribution includes the first use of graph neural networks (GNNs) with an attention mechanism to model the complex relationship between spoofing cues present in spectral and temporal domains. We propose an E2E spectro-temporal graph attention network called RawGAT-ST. RawGAT-ST model is further extended to an integrated spectro-temporal graph attention network, named AASIST which exploits the relationship between heterogeneous spectral and temporal graphs. Finally, this thesis proposes a novel data augmentation technique called RawBoost and uses a self-supervised, pre-trained speech model as a front-end to improve generalisation in the wild conditions
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Huang, Chi-Yang, and 黃啟陽. "Face Spoofing Detection from a Single Image Using Texture and Direction Analysis in HSV Color Space." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/46627897251475594634.

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碩士<br>國立交通大學<br>電控工程研究所<br>104<br>In the recent years, there are many liveness detection methods proposed to against photograph spoofing through analyzing the fundamental differences between human faces and printed faces. The differences in surface between human faces and printed faces are distinctive specular reflections and shades because a human face is a complex 3D object whereas a printed face can be seen as a planar object. Because an image recaptured from a photograph has the twice reflection, the gradient direction histogram of the image is different. Furthermore, the HSV color space can be more perfectly to deal with some information which human’s eyes cannot get and closer to the perception of humans. Therefore, we present to analyze facial image textures using multi-scale LBP and gradient direction analysis by Sobel operators on the illumination component of HSV color space for detecting whether there is a live person or a printed face in front of the camera. From our experiment results, we can see that the proposed feature can improve the face spoofing detection performance.
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Wang, Shun-Yi, and 王順億. "Face Spoofing Detection from a Single Image Based on Dual-Channel Texture and Color Distortion Analysis." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/wx8g7v.

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碩士<br>國立交通大學<br>電控工程研究所<br>105<br>In recent years, there are many spoofing face detection methods proposed to against 2D face spoofing attacks including printed photos and screen displayed photos through analyzing the differences between human faces and fake faces. The difference in light reflection between human faces and 2D fake faces mainly comes from distinctive shades and specular reflections because a human face is a complex 3D object whereas a 2D fake face is a flat surface. Besides, the color distribution of a fake face image is quite different to a real face due to the qualities of spoofing mediums such as a printer and a screen. For example, a printed face image usually has lower color contrast than a real face while a screen displayed face image has higher color contrast. For these different properties, this thesis proposes an approach by using the combination of two texture features and a color feature to determine whether there is a live person or a spoofing face in front of the camera. The two texture features include multi-scale LBP and the R-G deviated texture proposed in this thesis, while the color feature adopts the color moment, which is a measurement of the color distribution of an image. From the experimental results, the proposed approach indeed improves the performance of spoofing face detection.
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Wu, Tzu-Yuan, and 吳紫源. "A Deep-Learning-Based Face Liveness Detection System Against Spoofing Attack Using 2D Image Distortion Analysis." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/52dj7s.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>107<br>With the development of science and technology, face recognition is now an important technology for authentication in various access control applications, especially used in mobile devices. Unlocking by face has gradually replaced fingerprint identification in some scenarios, which becomes one of the major biometric authentication technology of mobile phones. In a common camera, due to the lack of depth information, it is easy to make fake face images to crack the identification system (e.g., paper printing and screen display) compared with other biological features such as fingerprints and palm prints. Therefore, face liveness detection against spoofing attack using 2D image distortion analysis will be a very important issue in the field of information security. By virtue of the different features between real faces and fake faces, this thesis adopts local binary pattern and 2D image distortion analysis to extract texture information of images, which are used for developing our face liveness detection system against spoofing attack to distinguish fake faces from real faces by a deep neural network. The system employs only a single image captured from a common camera to discriminant real faces and fake faces. In the experiments, three kinds of face spoofing databases are used as subjects of cross-validation. The methods and dataset made by ourselves presented in this thesis can effectively classify the authenticity of human faces. The accuracy of the inside test reaches 99.55%, while that of the outside test attains 95.13%. The experimental results show that our face liveness detection system has high accuracy and generality.
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Book chapters on the topic "Face spoofing detection"

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Yu, Su-Gyeong, So-Eui kim, Kun Ha Suh, and Eui Chul Lee. "Face Spoofing Detection Using DenseNet." In Intelligent Human Computer Interaction. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68452-5_24.

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Hernandez-Ortega, Javier, Julian Fierrez, Aythami Morales, and Javier Galbally. "Introduction to Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_9.

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Bhattacharjee, Sushil, Amir Mohammadi, André Anjos, and Sébastien Marcel. "Recent Advances in Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_10.

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Komulainen, Jukka, Zinelabidine Boulkenafet, and Zahid Akhtar. "Review of Face Presentation Attack Detection Competitions." In Handbook of Biometric Anti-Spoofing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_14.

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Yu, Zitong, Jukka Komulainen, Xiaobai Li, and Guoying Zhao. "Review of Face Presentation Attack Detection Competitions." In Handbook of Biometric Anti-Spoofing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5288-3_12.

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Komulainen, Jukka, Abdenour Hadid, and Matti Pietikäinen. "Face Spoofing Detection Using Dynamic Texture." In Computer Vision - ACCV 2012 Workshops. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37410-4_13.

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Costa-Pazo, Artur, Esteban Vazquez-Fernandez, José Luis Alba-Castro, and Daniel González-Jiménez. "Challenges of Face Presentation Attack Detection in Real Scenarios." In Handbook of Biometric Anti-Spoofing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_12.

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Heusch, Guillaume, and Sébastien Marcel. "Remote Blood Pulse Analysis for Face Presentation Attack Detection." In Handbook of Biometric Anti-Spoofing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_13.

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Nurnoby, M. Faisal, and El-Sayed M. El-Alfy. "Two-Stage Face Detection and Anti-spoofing." In Advances in Visual Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47969-4_35.

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Mangal, Shivani, and Khushboo Agarwal. "Face-Anti-spoofing Based on Liveness Detection." In Intelligent Cyber Physical Systems and Internet of Things. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18497-0_19.

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Conference papers on the topic "Face spoofing detection"

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Ju, Yurui. "Regional Face Recognition Algorithm Based on Ahash and Face Anti-Spoofing Detection." In 2024 4th International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV). IEEE, 2024. https://doi.org/10.1109/aivrv63595.2024.10859326.

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Kunwar, Arun, and Ajita Rattani. "Unified Face Matching and Physical-Digital Spoofing Attack Detection." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2025. https://doi.org/10.1109/wacvw65960.2025.00157.

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Mithun, M., Sumod Sundar, and Christy D. Ponnan. "College Library Face Detection and Management System with Anti-spoofing Mechanism." In 2024 Asian Conference on Intelligent Technologies (ACOIT). IEEE, 2024. https://doi.org/10.1109/acoit62457.2024.10941336.

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Jing, Jiuyao, Yu Zheng, Qi He, and Chunlei Peng. "Face Anti-spoofing based on Multi-modal Dual-stream Anomaly Detection." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00047.

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P, Poornima, and M. Tamilselvi. "Attention-Guided GANs for Robust Face Anti-Spoofing Detection in Unconstrained Environment." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035284.

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Li, Lan, Hongzhe Dou, Zijia Yu, Xujuan Zhang, Lijuan Sun, and Jundi Wang. "Silent Face Anti-Spoofing Detection Algorithm Based on Meta-learning and Attention Mechanism." In 2024 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2024. https://doi.org/10.1109/aeeca62331.2024.00075.

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Vannurswamy, K., B. H. Shekar, Bharathi Pilar, A. Karunakar Kotegar, and Frank Jiang. "Deep Ensemble Learning Approach for Face Anti-Spoofing Detection based on Pre-trained Models." In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI). IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10782399.

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Luevano, Luis S., Yoanna Martínez-Díaz, Heydi Méndez-Vázquez, Miguel González-Mendoza, and Davide Frey. "Assessing the Performance of Efficient Face Anti-Spoofing Detection Against Physical and Digital Presentation Attacks." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00108.

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Kumar, Biresh, Kumari Manisha, Anurag Sinha, Abhishek Kumar, and Jeevan Kumar. "Deep Learning Based Image Classification for Automated Face Spoofing Detection Using Machine Learning: Convolutional Neural Network." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692150.

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Phan, Quoc-Tin, Duc-Tien Dang-Nguyen, Giulia Boato, and Francesco G. B. De Natale. "FACE spoofing detection using LDP-TOP." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532388.

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