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presentation attack detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments

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SENSORS 2021年第22期21卷 7728页

作者： Kubicki, Kacper Kapusta, Pawel Slot, Krzysztof Lodz Univ Technol Inst Appl Comp Sci Stefanowskiego 18-22 PL-90001 Lodz Poland

The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge-response scheme. We propose a novel approach to convolutional phoneme classifier training, which ensures high phoneme recognition accuracy even for significantly simplified network architectures, thus enabling efficient utterance verification on resource-limited hardware, such as mobile phones or embedded devices. We consider Deep Convolutional Neural Networks operating on windows of speech Mel-Spectrograms as a means for phoneme recognition, and we show that one can boost the performance of highly simplified neural architectures by modifying the principle underlying training set construction. Instead of generating training examples by slicing spectrograms using a sliding window, as it is commonly done, we propose to maximize the consistency of phoneme-related spectrogram structures that are to be learned, by choosing only spectrogram chunks from the central regions of phoneme articulation intervals. This approach enables better utilization of the limited capacity of the considered simplified networks, as it significantly reduces a within-class data scatter. We show that neural architectures comprising as few as dozens of thousands parameters can successfully-with accuracy of up to 76%, solve the 39-phoneme recognition task (we use the English language TIMIT database for experimental verification of the method). We also show that ensembling of simple classifiers, using a basic bagging method, boosts the recognition accuracy by another 2-3%, offering Phoneme Error Rates at the level of 23%, which approaches the accuracy of the state-of-the-art deep neural architectures that are one to two orders of magnitude more complex than the proposed solution. This, in turn, enables executing reliable presentation attack detection, based on just few-syllable long challenges on highly resource-limited computing hardware.

关键词： biometrics presentation attack detection mel-spectrogram phoneme classification deep neural networks

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presentation attack detection based on two-stream vision transformers with self-attention fusion

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REpresentation 2022年 85卷

作者： Peng, Fei Meng, Shao-hua Long, Min Hunan Univ Sch Comp Sci & Elect Engn Changsha 410082 Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU , CASIA-MFSD , and Replay-attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.

关键词： presentation attack detection Multi-scale retinex with color restoration Vision transformer Deep learning Feature fusion

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Prototype-Guided Autoencoder for OCT-Based Fingerprint presentation attack detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023年 18卷 3461-3475页

作者： Liu, Yi-Peng Zuo, Wangyang Liang, Ronghua Sun, Haohao Li, Zhanqing Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Anti-spoofing ability is vital for fingerprint identification systems. Conventional fingerprint scanning devices can only obtain information from the fingertip surfaces, and their performance is susceptible to skin conditions and presentation attacks (PAs). However, optical coherence tomography (OCT) can scan subcutaneous tissue and obtain 3D fingerprint structures, naturally enhancing its PA detection (PAD) ability from the perspective of hardware. Existing unsupervised PAD methods are based on image reconstruction. However, the reconstruction error is easily affected by OCT noise and the rich details of OCT images. Therefore we propose feature-based reconstruction to alleviate this problem, called the prototype-guided autoencoder. The model consists of a memory module and a denoising autoencoder without the requirement of PA fingerprints. As only bona fide fingerprints are available during the training phase, the memory module contains the prototype features of the bona fide fingerprints. During the inference phase, as the prototype memory module is frozen, the reconstructed representation of the bona fide input is close to the bona fide fingerprint features. Calculating the distance between the original features and the prototype reconstructed representation of the sample can achieve PAD. To obtain a better decision making boundary, we propose a representation consistency constraint, which reduces the bona fide representation reconstruction distance closer, so that it is easier to differentiate between fingerprints and PAs.

关键词： Fingerprint recognition Feature extraction Image reconstruction Prototypes Memory modules Training Noise reduction presentation attack detection optical coherence tomography autoencoder prototype

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Audio-Visual Biometric Recognition and presentation attack detection: A Comprehensive Survey

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IEEE ACCESS 2021年 9卷 37431-37455页

作者： Mandalapu, Hareesh Reddy, Aravinda P. N. Ramachandra, Raghavendra Rao, Krothapalli Sreenivasa Mitra, Pabitra Prasanna, S. R. Mahadeva Busch, Christoph Norwegian Univ Sci & Technol NTNU Dept Informat Secur & Commun Technol N-2815 Gjovik Norway Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Technol Dharwad Dept Elect Engn Dharwad 580011 Karnataka India

Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and presentation attack detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.

关键词： Biometrics (access control) Face recognition Databases Feature extraction Visualization Biosensors Authentication Biometrics audio-visual person recognition presentation attack detection

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Fully supervised contrastive learning in latent space for face presentation attack detection

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APPLIED INTELLIGENCE 2023年第19期53卷 21770-21787页

作者： Alassafi, Madini O. Ibrahim, Muhammad Sohail Naseem, Imran AlGhamdi, Rayed Alotaibi, Reem Kateb, Faris A. Oqaibi, Hadi Mohsen Alshdadi, Abdulrahman A. Yusuf, Syed Adnan King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Love Data Res & Dev Karachi Pakistan Univ Western Australia Sch Elect Elect & Comp Engn Perth Australia Karachi Inst Econ & Technol Coll Engn Karachi Pakistan Univ Jeddah Coll Comp Sci & Engn Dept Informat Syst & Technol Jeddah Saudi Arabia Intelex Pvt Ltd Res & Innovat Dept Southampton England

The vulnerability of conventional face recognition systems to face presentation or face spoofing attacks has attracted a great deal of attention from information security, forensic, and biometric communities during the past few years. With the recent advancement and availability of cutting-edge computing technologies, sophisticated and computationally expensive solutions to many problems have been made possible. Accordingly, deep learning-based face presentation attack detection (PAD) methods have gained increasing popularity. In this research, we propose a supervised contrastive learning approach to tackle the face anti-spoofing problem. Essentially, the latent space encoding is achieved through an encoder network using the contrastive loss function infused with the class label information. The proposed robust encoding is followed by a simple classifier to distinguish between a real and a spoof face. To the best of our knowledge, this is the first work that uses fully supervised contrastive learning for the two-dimensional (2D) face PAD task. The performance of the proposed method is evaluated on several face anti-spoofing datasets and the results clearly show the efficacy of the proposed approach compared to other contemporary methods.

关键词： Deep learning Anti-spoofing Face liveness detection Supervised contrastive learning presentation attack detection

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Quality Matters: Boosting Face presentation attack detection With Image Quality Metrics

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IEEE ACCESS 2024年 12卷 94654-94672页

作者： Liu, Xinwei Wang, Renfang Liu, Wen Zhang, Liangbin Wang, Xiaoxia Zhejiang Wanli Univ Coll Big Data & Software Engn Dept Comp Sci Ningbo 315104 Peoples R China Zhejiang Wanli Univ Coll Informat & Intelligent Engn Ningbo 315104 Peoples R China Zhejiang Wanli Univ ZWU Lib & Learning Resource Ctr Ningbo 315104 Zhejiang Peoples R China

Face presentation attack detection (PAD) is critical for enhancing the security of facial recognition systems against sophisticated attacks. This study explores the use of general Image Quality Assessment (IQA) methods in face PAD, offering an alternative strategy that deviates from traditional, face-specific PAD techniques. Our evaluation of eight widely-used IQA methods across four PAD databases is structured around three distinct experimental protocols. Preliminary findings indicate that the general IQA methods are not fully effective in differentiating between genuine and attack samples, highlighting the need for modification. Nonetheless, a notable enhancement in performance is observed following the re-training of these methods using PAD datasets, bringing their effectiveness in line with that of advanced traditional PAD methods. This study provides evidence for the potential of general IQA in bolstering the resilience of face recognition systems against presentation attacks.

关键词： Biometrics face recognition image quality assessment presentation attack detection Biometrics face recognition image quality assessment presentation attack detection

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Mobile Contactless Fingerprint presentation attack detection: Generalizability and Explainability

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIE...

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IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE 2024年第4期6卷 561-574页

作者： Priesnitz, Jannis Casula, Roberto Kolberg, Jascha Fang, Meiling Madhu, Akhila Rathgeb, Christian Marcialis, Gian Luca Damer, Naser Busch, Christoph Hsch Darmstadt da sec Biometr & Secur Res Grp D-64295 Darmstadt Germany Univ Cagliari PRA Lab I-09123 Cagliari Italy Fraunhofer Inst Comp G Res IGD D-64283 Darmstadt Germany Yangzhou Univ Coll Informat Engn Yangzhou 225127 Peoples R China Fraunhofer Inst Comp G Res IGD D-64283 Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci D-64289 Darmstadt Germany

Contactless fingerprint recognition is an emerging biometric technology that has several advantages over contact-based schemes, such as improved user acceptance and fewer hygienic concerns. Like for most other biometrics, presentation attack detection (PAD) is crucial to preserving the trustworthiness of contactless fingerprint recognition methods. For many contactless biometric characteristics, Convolutional Neural Networks (CNNs) represent the state-of-the-art of PAD algorithms. For CNNs, the ability to accurately classify samples that are not included in the training is of particular interest, since these generalization capabilities indicate robustness in real-world scenarios. In this work, we focus on the generalizability and explainability aspects of CNN-based contactless fingerprint PAD methods. Based on previously obtained findings, we selected four CNN-based methods for contactless fingerprint PAD: two PAD methods designed for other biometric characteristics, an algorithm for contact-based fingerprint PAD and a general-purpose ResNet18. For our evaluation, we use four databases and partition them using Leave-One-Out (LOO) protocols. Furthermore, the generalization capability to a newly captured database is tested. Moreover, we explore t-SNE plots as a means of explainability to interpret our results in more detail. The low D-EERs obtained from the LOO experiments (below 0.1% D-EER for every LOO group) indicate that the selected algorithms are well-suited for the particular application. However, with an D-EER of 4.14%, the generalization experiment still has room for improvement.

关键词： Fingerprint recognition Biometrics (access control) Feature extraction Databases Classification algorithms Image color analysis Task analysis Contactless fingerprint recognition presentation attack detection generalizability

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Internal Structure Attention Network for Fingerprint presentation attack detection From Optical Coherence Tomography

IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIE...

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IEEE TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE 2023年第4期5卷 524-537页

作者： Sun, Haohao Zhang, Yilong Chen, Peng Wang, Haixia Liang, Ronghua Zhejiang Univ Technol Coll Informat & Engn Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Comp Sci Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Software Hangzhou 310023 Peoples R China

As a non-invasive optical imaging technique, optical coherence tomography (OCT) has proven promising for automatic fingerprint recognition system (AFRS) applications. Diverse approaches have been proposed for OCT-based finger-print presentation attack detection (PAD). However, considering the complexity and variety of PA samples, it is extremely challenging to increase the generalization ability with the limited PA dataset. To solve the challenge, this paper presents a novel supervised learning-based PAD method, denoted as internal structure attention PAD (ISAPAD). ISAPAD applies prior knowledge to guide network training. Specifically, the proposed dual-branch architecture in ISAPAD can not only learn global features from the OCT images, but also concentrate on the layered structure feature which come from the internal structure attention module (ISAM). The simple yet effective ISAM enables the network to obtain layered segmentation features exclusively belonging to Bonafide from noisy OCT volume data. By incorporating effective training strategies and PAD score generation rules, ISAPAD ensures reliable PAD performance even with limited training data. Extensive experiments and visualization analysis substantiate the effectiveness of the proposed method for OCT PAD.

关键词： Fingerprint neural network presentation attack detection optical coherence tomography

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A study on vulnerability and presentation attack detection in palmprint verification system

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PATTERN ANALYSIS AND APPLICATIONS 2018年第3期21卷 769-782页

作者： Bhilare, Shruti Kanhangad, Vivek Chaudhari, Narendra Indian Inst Technol Indore Discipline Comp Sci & Engn Indore Madhya Pradesh India Indian Inst Technol Indore Discipline Elect Engn Indore Madhya Pradesh India

As biometric systems become ubiquitous in the domain of personal authentication, it is of utmost importance that these systems are secured against attacks. Among various types of attacks on biometric systems, the presentation attack, which involves presenting a fake copy (artefact) of the real biometric to the biometric sensor to gain illegitimate access, is the most common one. Despite the serious threat posed by these attacks, not much work has been done to address this vulnerability in palmprint-based biometric systems. This paper demonstrates the vulnerability of a palmprint verification system to presentation attacks and proposes a novel presentation attack detection (PAD) approach to discriminating between real biometric samples and artefacts. The proposed PAD approach is inspired by a work that established relationship between the surface reflectance and a set of statistical features extracted from the image. Specifically, statistical features computed from the distributions of pixel intensities, sub-band wavelet coefficients and the grey-level co-occurrence matrix form the original feature set, and CFS-based feature selection approach selects the most discriminating feature subset. A trained binary classifier utilizes the selected feature subset to determine whether the acquired image is of real hand or an artefact. For performance evaluation, an antispoofing database-PALMspoof has been developed. This database comprises left- and right-hand images of 104 subjects, and three kinds of artefacts generated from these images. In addition to PALMspoof database, the biometric system's vulnerability has been assessed on display and print artefacts generated from two publicly available palmprint datasets. Our experimental results show that 1) the palmprint verification system is highly vulnerable with spoof acceptance of 84.56%;2) the proposed PAD approach is effective against both print and display attacks, in both same-device and cross-device scenarios;and 3) the

关键词： Biometrics Palmprint presentation attack detection Anti-spoofing Surface reflectance Image quality

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Fingerprint presentation attack detection by Region Decomposition

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 3974-3985页

作者： Fei, Hongyan Huang, Chuanwei Wu, Song Wang, Zheng Jia, Zexi Feng, Jufu Peking Univ Sch Intelligence Sci & Technol Minist Educ Key Lab Machine PerceptNatl Key Lab Gen Artificia Beijing 100871 Peoples R China

Fingerprint presentation attack detection (PAD) is a crucial step in automatic fingerprint identification systems, which safeguards users from unauthorized malicious access. However, current presentation attack (i.e. spoof) techniques can forge intricate details of fingerprints (such as sweat holes), which makes the artifact evidence harder to detect. In this paper, we propose a novel PAD method from the perspective of decomposition to highlight the artifact evidence in each constituent element. Specifically, we utilize the fingerprint enhancement to decompose the fingerprint into the ridge region and the edge region. We observe that artifact evidence mainly exists in the gradient field within the ridge region, while it primarily resides in the spatial domain within the edge region. Then we propose an Orientation-Based Central Difference Convolution (OB-CDC) layer to prioritize gradient variations along the ridge direction. To further enhance robustness, we propose a Minutia Patches Random Rotation (MPRR) operation to disrupt the identity information of the fingerprint while preserving the artifact evidence. By integrating these techniques, we propose a two-stream network called presentation-attack-detection-with-Region-Decomposition-Network (PADRD-Net) which integrates the processed feature of the ridge region and the edge region through a halfway fusion ResNet-18 structure. Experimental results on the LivDet 2021 dataset show that our proposed PADRD-Net can achieve 20.39% on BPCER@APCER = 1% and 87.12% on TDR@FDR = 1%, significantly outperforms the state-of-the-art. We also achieve outstanding performance in both the cross-sensor scenario and the cross-sensor and cross-material scenario. Extensive ablation studies and analysis experiments further indicate the effectiveness and robustness of our method.

关键词： Fingerprint recognition Feature extraction Convolution Image edge detection Task analysis Faces Estimation Fingerprint presentation attack detection decomposition multi-domain OB-CDC layer minutia patches random rotation

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