Prototype-Guided Autoencoder for OCT-Based Fingerprint Presentation Attack Detection

Automated Fingerprint Recognition Systems (AFRSs) have been threatened by Presentation Attack (PA) since its existence. It is thus desirable to develop effective presentation attack detection (PAD) methods. However, the unpredictable PAs make PAD be a challenging problem. This paper proposes a novel One-Class PAD (OCPAD) method for Optical Coherence Technology (OCT) images based fingerprint PA detection. The proposed OCPAD model is learned from a training set only consists of Bonafides (i.e. real fingerprints). The reconstruction error and latent code obtained from the trained auto-encoder network in the proposed model is taken as the basis for the following spoofness score calculation. To get more accurate reconstruction error, we propose an activation map based weighting model to further refine the accuracy of reconstruction error. We test different statistics and distance measures and finally use a decision level fusion to make the final prediction. Our experiments are performed using a dataset with 93200 bonafide scans and 48400 PA scans. The results show that the proposed OCPAD can achieve a True Positive Rate (TPR) of 99.43% when the False Positive Rate (FPR) equals to 10% and a TPR of 96.59% when FPR=5%, which significantly outperformed a feature based approach and a supervised learning based model requiring PAs for training.

The rapid advancement of premium smartphones with sophisticated high resolution cameras has enabled reliable and convenient contactless acquisition of fingerprint biometrics, making them suitable for verification at scale. However, as with other biometric systems, contactless fingerprint verification remains vulnerable to presentation attacks. Many Presentation Attack Detection (PAD) methods rely on supervised learning that requires labelled attack data, which limits generalisation. We propose an unsupervised PAD approach based on the Denoising Diffusion Probabilistic Model (DDPM). The system is trained only on bona fide samples and makes decisions via score level fusion of two deep similarity measures, yielding robustness to unseen attacks without access to attack labels. We further provide a Jacobian based theoretical analysis that explains why diffusion reconstructions are well suited to PAD. Using Frobenius and spectral norms together with a log determinant surrogate, we show that the iterative refinement in diffusion models preserves ridge aligned structure while contracting off manifold artifacts, thereby accounting for the observed empirical gains over single shot alternatives. In addition, we introduce a new fingerphoto presentation attack dataset comprising 101 subjects with 6,060 bona fide and 1,818 attack images captured on recent smartphones under diverse conditions. Benchmarking across four datasets shows that the proposed method is effective in practice, improving PAD performance and outperforming strong supervised baselines and recent state of the art methods in unseen attack scenarios. We report uncertainty estimates and non parametric statistical summaries across datasets and PAIs (Presentation Attack Instruments), which indicate consistent advantages without significant regressions, supporting the suitability of diffusion based PAD for deployment.

Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation attacks using printed and displayed facial photographs, 3D-printed, custom flexible 3D-latex and silicone masks is provided. The paper presents the intensity analysis of thermal energy distribution for specific facial landmarks during long-lasting experiments. Thermalization impact, as well as varying the subject’s state due to physical effort on presentation attack detection are investigated. A new thermal face spoofing dataset is introduced. Finally, a two-step deep learning-based method for the detection of presentation attacks is presented. Validation results of a set of deep learning methods across various presentation attack instruments are presented.

Today, face biometric systems are becoming widely accepted as a standard method for identity authentication in many security settings. For example, their deployment in automated border control gates plays a crucial role in accurate document authentication and reduced traveler flow rates in congested border zones. The proliferation of such systems is further spurred by the advent of portable devices. On the one hand, modern smartphone and tablet cameras have in-built user authentication applications while on the other hand, their displays are being consistently exploited for face spoofing. Similar to biometric systems of other physiological biometric identifiers, face biometric systems have their own unique set of potential vulnerabilities. In this work, these vulnerabilities (presentation attacks) are being explored via a biologically-inspired presentation attack detection model which is termed “BIOPAD.” Our model employs Gabor features in a feedforward hierarchical structure of layers that progressively process and train from visual information of people's faces, along with their presentation attacks, in the visible and near-infrared spectral regions. BIOPAD's performance is directly compared with other popular biologically-inspired layered models such as the “Hierarchical Model And X” (HMAX) that applies similar handcrafted features, and Convolutional Neural Networks (CNN) that discover low-level features through stochastic descent training. BIOPAD shows superior performance to both HMAX and CNN in all of the three presentation attack databases examined and these results were consistent in two different classifiers (Support Vector Machine and k-nearest neighbor). In certain cases, our findings have shown that BIOPAD can produce authentication rates with 99% accuracy. Finally, we further introduce a new presentation attack database with visible and near-infrared information for direct comparisons. Overall, BIOPAD's operation, which is to fuse information from different spectral bands at both feature and score levels for the purpose of face presentation attack detection, has never been attempted before with a biologically-inspired algorithm. Obtained detection rates are promising and confirm that near-infrared visual information significantly assists in overcoming presentation attacks.

The main scope of this chapter is to serve as an introduction to face presentation attack detection, including key resources and advances in the field in the last few years. The next pages present the different presentation attacks that a face recognition system can confront, in which an attacker presents to the sensor, mainly a camera, a Presentation Attack Instrument (PAI), that is generally a photograph, a video, or a mask, with the target to impersonate a genuine user or to hide the actual identity of the attacker via obfuscation. First, we make an introduction of the current status of face recognition, its level of deployment, and its challenges. In addition, we present the vulnerabilities and the possible attacks that a face recognition system may be exposed to, showing that way the high importance of presentation attack detection methods. We review different types of presentation attack methods, from simpler to more complex ones, and in which cases they could be effective. Then, we summarize the most popular presentation attack detection methods to deal with these attacks. Finally, we introduce public datasets used by the research community for exploring vulnerabilities of face biometrics to presentation attacks and developing effective countermeasures against known PAIs.

The Presentation attacks on biometric systems have become a major concern in the cyber world. The literature reveals that these systems are vulnerable to spoofing or presentation attacks (PAs) which sometimes result in complete failure of the recognition system. To combat against PAs, the anti-spoofing or Presentation attack detection (PAD) methods have been developed to check the liveness of the fingerprint modality. However, with the emergence of artificial intelligence, several software-based countermeasures have been proposed by the research community. Depending on the type feature extraction mechanism the PAD techniques are categorized into two main types namely; Machine learning (ML) and Deep Learning (DL) based approaches. This paper aims to present a review of existing work on the fingerprint PAD mechanism which has been carried out. The paper discusses the various fingerprint artifacts, state-of-the-art ML and DL based PAD algorithms, and publically available benchmark datasets. Furthermore, we discuss the major research challenges and future work that need to be addressed in this active field of fingerprint liveness detection.

Biometric systems are vulnerable to presentation attacks (PAs) performed using various PA instruments (PAIs). Even though there are numerous PA detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with de-folding and de-mixing to derive the task-specific representation for PAD. During de-folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing the generative loss. While de-mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing the interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with the state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% equal error rate (EER) in OULU-NPU and MSU-MFSD, exceeding the baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.

Face Recognition Systems (FRS) are vulnerable to spoofing attacks (a.k.a presentation attacks), which can be carried out by presenting a printed photo (print-photo), displaying a photo (display-photo), or displaying a video (replay-video). The issue of presentation attacks can be alleviated by algorithms known as presentation attack detection (PAD) mechanisms. In this paper, we propose a novel framework based on Hierarchical Cosine/Spherical Linear Interpolation of deep learning feature vectors followed by training a Linear SVM for PAD Classification. The deep learning feature vectors are extracted from existing networks trained on the Imagenet dataset. Our proposed approach hierarchically interpolates the extracted feature vectors using Cosine/Spherically Linear Interpolation, followed by using a Linear SVM for classification, and sum-rule fusion for generating final scores. We show our results on cross-dataset PAD for the classifier trained on OULU P1 Dataset and tested on Replay Mobile Dataset. We compare it with the current state-of-the-art (SOTA) algorithms published in the literature and achieve considerably lower detection error-rate (D-EER). The extraction of features from pre-trained networks makes our approach simple to use, apart from it, giving highly accurate results, which are much better than current SOTA.

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Presentations and attacks, and spoofs, oh my

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

State-of-the-art spoof detection methods tend to overfit to the spoof types seen during training and fail to generalize to unknown spoof types. Given that face anti-spoofing is inherently a local task, we propose a face anti-spoofing framework, namely Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework improves generalizability while maintaining the computational efficiency of holistic face anti-spoofing approaches (< 4 ms on a Nvidia GTX 1080Ti GPU). The proposed method is interpretable since it localizes which parts of the face are labeled as spoofs. Experimental results show that SSR-FCN can achieve TDR = 65% @ 2.0% FDR when evaluated on a dataset comprising of 13 different spoof types under unknown attacks while achieving competitive performances under standard benchmark datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).

The vulnerability of current face recognition systems to presentation attacks significantly limits their application in biometrics. Herein, we present a passive presentation attack detection method based on a complete plenoptic imaging system which can derive the complete plenoptic function of light rays using a single detector. Moreover, we constructed a multi-dimensional face database with 50 subjects and seven different types of presentation attacks. We experimentally demonstrated that our approach outperforms the state-of-the-art methods on all types of presentation attacks.

Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real-world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats, presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as the presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere with its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different levels of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarize the most popular types of attacks including the main challenges to address. Second, we present a taxonomy of PAD methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into iris recognition systems according to the most important scenarios of practical application.

As the need for contactless biometric authentication becomes more significant during COVID-19, and beyond, the popular biometric authentication method for mobile devices, iris detection, and facial recognition confronts various usability, security, and privacy concerns, including mask-wearing and various Presentation Attacks (PA). Specifically, liveness detection against spoofed artifacts is one of the most challenging tasks as many existing methods cannot conclusively assess the user's physical presence in unsupervised environments. Even though several methods have been proposed for tackling PA with motion challenges and 3D mapping, most of them require expensive depth sensors and fail to detect sophisticated 3D reconstruction attacks. We present a software-based face PA Detection (PAD) method named Eyes Show What Your Eyes See (Y-EYES), which creates challenges and detects meaningful corneal specular reflection responses from human eyes. To detect human liveness, Y-EYES creates multiple screen image patterns as a challenge, then captures the response of corneal specular reflections using the front camera and analyzes the images using lightweight Machine Learning (ML) techniques. Y-EYES system components include challenge pattern generation, reflection image augmentation (e.g., super-resolution), and ML-based analyses. We have implemented Y-EYES as Android, iOS, and web apps. Our extensive experimental results show that Y-EYES achieves liveness detection with high accuracy at around 200 ms against various types of sophisticated PA. Y-EYES liveness detection can be applied for multiple contactless biometric authentications accurately and efficiently without any costly extra sensors.