Journal articles: 'Face spoofing'

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Qin, Yunxiao, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, and Zhen Lei. "Learning Meta Model for Zero- and Few-Shot Face Anti-Spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11916–23. http://dx.doi.org/10.1609/aaai.v34i07.6866.

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Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.

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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 (May 2022): 517–25. http://dx.doi.org/10.53106/160792642022052303010.

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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.

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Megawan, Sunario, Wulan Sri Lestari, and Apriyanto Halim. "Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN." Journal of Information System Research (JOSH) 3, no. 3 (April 29, 2022): 291–99. http://dx.doi.org/10.47065/josh.v3i3.1519.

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Face non-spoofing detection is an important job used to ensure authentication security by performing an analysis of the captured faces. Face spoofing is the process of fake faces by other people to gain illegal access to the biometric system which can be done by displaying videos or images of someone's face on the monitor screen or using printed images. There are various forms of attacks that can be carried out on the face authentication system in the form of face sketches, face photos, face videos and 3D face masks. Such attacks can occur because photos and videos of faces from users of the facial authentication system are very easy to obtain via the internet or cameras. To solve this problem, in this research proposes a non-spoofing face detection model on video using Faster R-CNN. The results obtained in this study are the Faster R-CNN model that can detect non-spoof and spoof face in real time using the Raspberry Pi as a camera with a frame rate of 1 fps.

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Bok, Jin Yeong, Kun Ha Suh, and Eui Chul Lee. "Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance." Electronics 9, no. 4 (April 17, 2020): 661. http://dx.doi.org/10.3390/electronics9040661.

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Face recognition is a representative biometric that can be easily used; however, spoofing attacks threaten the security of face biometric systems by generating fake faces. Thus, it is not advisable to only consider sophisticated spoofing cases, such as three-dimensional masks, because they require additional equipment, thereby increasing the implementation cost. To prevent easy face spoofing attacks through print and display, the two-dimensional (2D) image analysis method using existing face recognition systems is reasonable. Therefore, we proposed a new database called the “pattern recognition-face spoofing advancement database” that can be used to prevent such attacks based on 2D image analysis. To the best of our knowledge, this is the first face spoofing database that considers the changes in both the angle and distance. Therefore, it can be used to train various positional relationships between a face and camera. We conducted various experiments to verify the efficiency of this database. The spoofing detection accuracy of our database using ResNet-18 was found to be 96.75%. The experimental results for various scenarios demonstrated that the spoof detection performances were better for images with pinch angle, near distance images, and replay attacks than those for front images, far distance images, and print attacks, respectively. In the cross-database verification result, the performance when tested with other databases (DBs) after training with our DB was better than the opposite. The results of cross-device verification in terms of camera type showed negligible difference; thus, it was concluded that the type of image sensor does not affect the detection accuracy. Consequently, it was confirmed that the proposed DB that considers various distances, capture angles, lighting conditions, and backgrounds can be used as a training DB to detect spoofing attacks in general face recognition systems.

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Abusham, Eimad, Basil Ibrahim, Kashif Zia, and Muhammad Rehman. "Facial Image Encryption for Secure Face Recognition System." Electronics 12, no. 3 (February 3, 2023): 774. http://dx.doi.org/10.3390/electronics12030774.

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A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks.

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Perdana, Rizky Naufal, Igi Ardiyanto, and Hanung Adi Nugroho. "A Review on Face Anti-Spoofing." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (June 18, 2021): 29. http://dx.doi.org/10.22146/ijitee.61827.

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The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

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

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

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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (March 1, 2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.p1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.

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

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

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Aziz, Azim Zaliha Abd, and Mohd Rizon Mohamed Juhari. "Face spoofing detection using surface and sub-surface reflections analysis." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (October 1, 2021): 189. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp189-197.

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Reflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multi-reflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials.

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Veena, S., S. Saranya, D. Vivek, Rishabh Das, and Dipanshu Gupta. "Face Recognition with Anti-Spoofing." Journal of Computational and Theoretical Nanoscience 15, no. 8 (August 1, 2018): 2665–70. http://dx.doi.org/10.1166/jctn.2018.7519.

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Shao, Rui, Xiangyuan Lan, and Pong C. Yuen. "Regularized Fine-Grained Meta Face Anti-Spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11974–81. http://dx.doi.org/10.1609/aaai.v34i07.6873.

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Face presentation attacks have become an increasingly critical concern when face recognition is widely applied. Many face anti-spoofing methods have been proposed, but most of them ignore the generalization ability to unseen attacks. To overcome the limitation, this work casts face anti-spoofing as a domain generalization (DG) problem, and attempts to address this problem by developing a new meta-learning framework called Regularized Fine-grained Meta-learning. To let our face anti-spoofing model generalize well to unseen attacks, the proposed framework trains our model to perform well in the simulated domain shift scenarios, which is achieved by finding generalized learning directions in the meta-learning process. Specifically, the proposed framework incorporates the domain knowledge of face anti-spoofing as the regularization so that meta-learning is conducted in the feature space regularized by the supervision of domain knowledge. This enables our model more likely to find generalized learning directions with the regularized meta-learning for face anti-spoofing task. Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

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Rusia, Mayank Kumar, and Dushyant Kumar Singh. "A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks." Cybernetics and Information Technologies 22, no. 3 (September 1, 2022): 127–45. http://dx.doi.org/10.2478/cait-2022-0032.

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Abstract Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.

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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 (March 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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H, Vinutha, and Thippeswamy G. "Antispoofing in face biometrics: a comprehensive study on software-based techniques." Computer Science and Information Technologies 4, no. 1 (March 1, 2023): 1–13. http://dx.doi.org/10.11591/csit.v4i1.pp1-13.

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The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop antispoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.

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

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

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Abduh, Latifah, Luma Omar, and Ioannis Ivrissimtzis. "Anomaly Detection with Transformer in Face Anti-spoofing." Journal of WSCG 31, no. 1-2 (July 2023): 91–98. http://dx.doi.org/10.24132/jwscg.2023.10.

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Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNet as the backbone for anomaly detection in face anti-spoofing and compare the performance of various one-class classifiers at the end of the pipeline, such as one-class SVM, Isolation Forest, and decoders. Test results on the RA and SiW databases show the proposed approach to be competitive as an anomaly detection method for face anti-spoofing.

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

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

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Solomon, Enoch, and Krzysztof J. Cios. "FASS: Face Anti-Spoofing System Using Image Quality Features and Deep Learning." Electronics 12, no. 10 (May 12, 2023): 2199. http://dx.doi.org/10.3390/electronics12102199.

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Face recognition technology has been widely used due to the convenience it provides. However, face recognition is vulnerable to spoofing attacks which limits its usage in sensitive application areas. This work introduces a novel face anti-spoofing system, FASS, that fuses results of two classifiers. One, random forest, uses the identified by us seven no-reference image quality features derived from face images and its results are fused with a deep learning classifier results that uses entire face images as input. Extensive experiments were performed to compare FASS with state-of-the-art anti-spoofing systems on five benchmark datasets: Replay-Attack, CASIA-MFSD, MSU-MFSD, OULU-NPU and SiW. The results show that FASS outperforms all face anti-spoofing systems based on image quality features and is also more accurate than many of the state-of-the-art systems based on deep learning.

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Raheem, Enas A., Sharifah Mumtazah Syed Ahmad, and Wan Azizun Wan Adnan. "Insight on face liveness detection: A systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 5865. http://dx.doi.org/10.11591/ijece.v9i6.pp5865-5175.

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To review researcher’s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature. The final number of articles considered is 65 articles. 4 of them where review and survey articles that described a general overview about liveness detection and anti-spoofing methods. Since 2012, and despite of leaving some areas unestablished and needs more attention, researchers tried to keep track of liveness detection in several ways. No matter what their category is, articles concentrated on challenges that faces the full utility of anti-spoofing methods and recommended some solutions to overcome these challenges. In this paper, different types of liveness detection and face anti-spoofing techniques are investigated to keep researchers updated with what is being developed in this field.

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Mohamed, Shaimaa, Amr Ghoneim, and Aliaa Youssif. "Visible/Infrared face spoofing detection using texture descriptors." MATEC Web of Conferences 292 (2019): 04006. http://dx.doi.org/10.1051/matecconf/201929204006.

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With extensive applications of face recognition technologies, face anti-spoofing played an important role and has drawn a great attention in the security systems. This study represents a multi-spectral face anti-spoofing method working with both visible (VIS) and near-infrared (NIR) spectra imaging. Spectral imaging is the capture of images in multiple bands. Since these attacks are carried out at the sensor, operating in the visible range, a sensor operating in another band can give more cues regarding the artifact or disguise used to carry out the attack. Our experimental results of public datasets proved that the proposed algorithms gain promising results for different testing scenarios and that our methods can deal with different illuminations and both photo and screen spoofing.

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

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

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Hatture, Sanjeeva Kumar M., and Shweta Policepatil. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (July 30, 2021): 225–32. http://dx.doi.org/10.35940/ijrte.b6309.0710221.

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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade attack. The masquerade attack is performed using authorized users’ artifact biometric data that may be artifact facial masks, photo or iris photo or any latex finger. This type of attack in Liveness detection has become counter problem in the today's world. To prevent such spoofing attack, we proposed Liveness detection of face by considering the countermeasures and texture analysis of face and also a hybrid approach which combine both passive and active liveness detection is used. Our proposed approach achieves accuracy of 99.33 percentage for face anti-spoofing detection. Also we performed active face spoofing by providing several task (turn face left, turn face right, blink eye, etc) that performed by user on live camera for liveness detection.

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Erdogmus, Nesli, and Sebastien Marcel. "Spoofing Face Recognition With 3D Masks." IEEE Transactions on Information Forensics and Security 9, no. 7 (July 2014): 1084–97. http://dx.doi.org/10.1109/tifs.2014.2322255.

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Chen, Haonan, Yaowu Chen, Xiang Tian, and Rongxin Jiang. "A Cascade Face Spoofing Detector Based on Face Anti-Spoofing R-CNN and Improved Retinex LBP." IEEE Access 7 (2019): 170116–33. http://dx.doi.org/10.1109/access.2019.2955383.

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Kong, Yueping, Xinyuan Li, Guangye Hao, and Chu Liu. "Face Anti-Spoofing Method Based on Residual Network with Channel Attention Mechanism." Electronics 11, no. 19 (September 25, 2022): 3056. http://dx.doi.org/10.3390/electronics11193056.

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The face recognition system is vulnerable to spoofing attacks by photos or videos of a valid user face. However, edge degradation and texture blurring occur when non-living face images are used to attack the face recognition system. With this in mind, a novel face anti-spoofing method combines the residual network and the channel attention mechanism. In our method, the residual network extracts the texture differences of features between face images. In contrast, the attention mechanism focuses on the differences of shadow and edge features located on nasal and cheek areas between living and non-living face images. It can assign weights to different filter features of the face image and enhance the ability of network extraction and expression of different key features in the nasal and cheek regions, improving detection accuracy. The experiments were performed on the public face anti-spoofing datasets of Replay-Attack and CASIA-FASD. We found the best value of the parameter r suitable for face anti-spoofing research is 16, and the accuracy of the method is 99.98% and 97.75%, respectively. Furthermore, to enhance the robustness of the method to illumination changes, the experiment was also performed on the datasets with light changes and achieved a good result.

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Hashmi, Mohammad Adil ullah. "Study of Machine Learning Algorithm based on Face Anti-Spoofing Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 8, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28013.

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Face spoofing detection is one of the most well-studied problems in computer vision. Face recognition has become a widely adopted technique in biometric authentication systems. In face recognition based authentication techniques, the system first recognized the person to verify the legitimacy of the user before granting access to the system resources. The system must be able to determine the liveness of the person in front of the camera, for example, by recognizing the face and denying the types of face presentation attacks related to photographs, videos and the 3D mask of the targeted person. Attackers try to directly or indirectly, masquerade the biometric system as another person by forging biometric traits and get unauthorized access. This work studies computer vision- based feature extraction techniques for real and spoof face imaging and combines different features in the area of face anti-spoofing. Keywords: - Face Spoofing, Face Recognition, Machine Learning (ML)

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Narayanamma Pydimani Lakshmi, N. "Innovative Face Anti - Spoofing: DRL Strategies for Enhanced Security." International Journal of Science and Research (IJSR) 13, no. 2 (February 5, 2024): 128–31. http://dx.doi.org/10.21275/sr24130101346.

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Shinde, Pratibha, and Ajay R. Raundale. "Face and liveness detection with criminal identification using machine learning and image processing techniques for security system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (March 1, 2024): 722. http://dx.doi.org/10.11591/ijai.v13.i1.pp722-729.

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In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.

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

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

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Albakri, Ghazel, and Sharifa Alghowinem. "The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras." Sensors 19, no. 8 (April 24, 2019): 1928. http://dx.doi.org/10.3390/s19081928.

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Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.

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Jia, Shan, Chuanbo Hu, Xin Li, and Zhengquan Xu. "Face spoofing detection under super-realistic 3D wax face attacks." Pattern Recognition Letters 145 (May 2021): 103–9. http://dx.doi.org/10.1016/j.patrec.2021.01.021.

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Desai, Khyati Jash, and Sunil Kumar. "A Study on Face Recognition and Face Spoofing Detection Techniques." International Journal of Computer Applications 185, no. 14 (June 20, 2023): 24–29. http://dx.doi.org/10.5120/ijca2023922823.

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Gwak, Yongjae, Chanho Jeong, Jong-hyuk Roh, Sangrae Cho, and Wonjun Kim. "Face Anti-spoofing Using Deep Dual Network." IEIE Transactions on Smart Processing & Computing 9, no. 3 (June 30, 2020): 203–11. http://dx.doi.org/10.5573/ieiespc.2020.9.3.203.

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Quan, Ruijie, Yu Wu, Xin Yu, and Yi Yang. "Progressive Transfer Learning for Face Anti-Spoofing." IEEE Transactions on Image Processing 30 (2021): 3946–55. http://dx.doi.org/10.1109/tip.2021.3066912.

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Wang, Jingjing, Jingyi Zhang, Ying Bian, Youyi Cai, Chunmao Wang, and Shiliang Pu. "Self-Domain Adaptation for Face Anti-Spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 2746–54. http://dx.doi.org/10.1609/aaai.v35i4.16379.

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Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or domain generalization (DG) techniques to address this problem. However, the target domain is often unknown during training which limits the utilization of DA methods. DG methods can conquer this by learning domain invariant features without seeing any target data. However, they fail in utilizing the information of target data. In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference. Specifically, a domain adaptor is designed to adapt the model for test domain. In order to learn a better adaptor, a meta-learning based adaptor learning algorithm is proposed using the data of multiple source domains at the training step. At test time, the adaptor is updated using only the test domain data according to the proposed unsupervised adaptor loss to further improve the performance. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

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Cai, Rizhao, Zhi Li, Renjie Wan, Haoliang Li, Yongjian Hu, and Alex C. Kot. "Learning Meta Pattern for Face Anti-Spoofing." IEEE Transactions on Information Forensics and Security 17 (2022): 1201–13. http://dx.doi.org/10.1109/tifs.2022.3158551.

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Tirunagari, Santosh, Norman Poh, David Windridge, Aamo Iorliam, Nik Suki, and Anthony T. S. Ho. "Detection of Face Spoofing Using Visual Dynamics." IEEE Transactions on Information Forensics and Security 10, no. 4 (April 2015): 762–77. http://dx.doi.org/10.1109/tifs.2015.2406533.

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Boulkenafet, Zinelabidine, Jukka Komulainen, and Abdenour Hadid. "Face Spoofing Detection Using Colour Texture Analysis." IEEE Transactions on Information Forensics and Security 11, no. 8 (August 2016): 1818–30. http://dx.doi.org/10.1109/tifs.2016.2555286.

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Zhang, Meigui, Kehui Zeng, and Jinwei Wang. "A Survey on Face Anti-Spoofing Algorithms." Journal of Information Hiding and Privacy Protection 2, no. 1 (2020): 21–34. http://dx.doi.org/10.32604/jihpp.2020.010467.

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Wang, Yan, Fudong Nian, Teng Li, Zhijun Meng, and Kongqiao Wang. "Robust face anti-spoofing with depth information." Journal of Visual Communication and Image Representation 49 (November 2017): 332–37. http://dx.doi.org/10.1016/j.jvcir.2017.09.002.

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Kose, Neslihan, and Jean-Luc Dugelay. "Mask spoofing in face recognition and countermeasures." Image and Vision Computing 32, no. 10 (October 2014): 779–89. http://dx.doi.org/10.1016/j.imavis.2014.06.003.

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Li, Haoliang, Wen Li, Hong Cao, Shiqi Wang, Feiyue Huang, and Alex C. Kot. "Unsupervised Domain Adaptation for Face Anti-Spoofing." IEEE Transactions on Information Forensics and Security 13, no. 7 (July 2018): 1794–809. http://dx.doi.org/10.1109/tifs.2018.2801312.

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Edmunds, Taiamiti, and Alice Caplier. "Face spoofing detection based on colour distortions." IET Biometrics 7, no. 1 (December 8, 2017): 27–38. http://dx.doi.org/10.1049/iet-bmt.2017.0077.

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Verissimo, Sandoval, Guilherme Gadelha, Leonardo Batista, João Janduy, and Fabio Falcão. "Transfer learning for face anti-spoofing detection." IEEE Latin America Transactions 21, no. 4 (April 2023): 530–36. http://dx.doi.org/10.1109/tla.2023.10128884.

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Deng, Pengchao, Chenyang Ge, Hao Wei, Yuan Sun, and Xin Qiao. "Multimodal contrastive learning for face anti-spoofing." Engineering Applications of Artificial Intelligence 129 (March 2024): 107600. http://dx.doi.org/10.1016/j.engappai.2023.107600.

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Kamat, Chanchala. "Face Anti-Spoofing Methods: A Comparative Analysis through the Lens of a Comprehensive Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 514–26. http://dx.doi.org/10.22214/ijraset.2024.58383.

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Abstract: In computer vision, face anti-spoofing is an important aspect that aims to differentiate genuine facial features from spoofing attempts. This review paper comprehensively explores existing methodologies, emphasising advancements in computer vision and deep learning. Diverse techniques, ranging from traditional methods like multi-scale LBPs and CNNs to recent innovations such as FeatherNet and ViT-S-Adapter-TSR, are meticulously analysed. A comparative table provides insights into different methods, highlighting their performance on various datasets like MSU-MFD, CASIA-FASD, and OULU-NPU. However, challenges like diverse datasets, varying evaluation metrics, and real-world applicability are acknowledged. The paper discusses limitations related to real-world conditions, computational efficiency, and the ever-evolving nature of spoofing techniques. It emphasises the need for ongoing collaboration and innovation in research to address challenges like dataset consistency and adaptability to emerging threats. In conclusion, while progress has been made, the paper emphasises the dynamic nature of face anti-spoofing research. The pursuit of more effective, adaptable, and computationally efficient methods continues, promising real-world impact against evolving threats.

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Li, Yidong, Wenhua Liu, Yi Jin, and Yuanzhouhan Cao. "SPGAN: Face Forgery Using Spoofing Generative Adversarial Networks." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (March 31, 2021): 1–20. http://dx.doi.org/10.1145/3432817.

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Current face spoof detection schemes mainly rely on physiological cues such as eye blinking, mouth movements, and micro-expression changes, or textural attributes of the face images [9]. But none of these methods represent a viable mechanism for makeup-induced spoofing, especially since makeup has been widely used. Compared with face alteration techniques such as plastic surgery, makeup is non-permanent and cost efficient, which makes makeup-induced spoofing become a realistic threat to the integrity of a face recognition system. To solve this problem, we propose a generative model to construct spoofing face images (confusing face images) for improving the accuracy and robustness of automatic face recognition. Our network structure is composed of two separate parts, with one using inter-attention mechanism to obtain interested face region, and another using intra-attention to translate imitation style with preserving imitation style-excluding details. These two attention mechanisms can precisely learn imitation style, where inter-attention pays more attention to imitation regions of image and intra-attention learns face attributes with long distance in image. To effectively discriminate generated images, we introduce an imitation style discriminator. Our model (SPGAN) generates face images that transfer the imitation style from target to subject image and preserve the imitation-excluding features. Experimental results demonstrate the performance of our model in improving quality of imitated face images.

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Shuting Wei, Shuting Wei, Zhiyuan Shi Shuting Wei, Zhibin Gao Zhiyuan Shi, Sheng Zhang Zhibin Gao, and Lianfen Huang Sheng Zhang. "A Domain Generalization Method Based on Hybrid Meta-Learning for Face Anti-Spoofing." 電腦學刊 33, no. 5 (October 2022): 083–93. http://dx.doi.org/10.53106/199115992022103305008.

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For face anti-spoofing, many methods have been proposed to improve the security of face recognition systems. Due to distribution discrepancies among different domains, it is difficult to seek a generalized space which can generalize well to unseen attacks. In this paper, we propose a framework based on meta-learning method to improve the generalization ability of face anti-spoofing. The feature extractor is trained with forcing the distribution of real faces more compact while the distribution of fake faces is more dispersed among domains. Then we add a hybrid-domain meta learner module to simulate multiple domain shift scenarios. Moreover, we add a refined triplet mining to constrain the distance between real faces and fake ones. Multiple gradient information is integrated to optimize the feature extractor and train the model with good generalization performance to unseen attacks of various scenarios. Extensive experiments on four public datasets show that our proposed method can get better generalization ability to unseen target domain compared with state-of-the-art methods.

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Journal articles on the topic 'Face spoofing'

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