Presentation Attack Detection Systems Research Articles

Deep learning techniques for biometric security: A systematic review of presentation attack detection systems

Biometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric systems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with presentation attacks (PAs) being prevalent and easily executed. PAs involve displaying videos, images, or full-face masks to trick biometric systems and gain unauthorized access. Many authors are currently focusing on detecting these presentation attacks (PAD) and have developed several methods, particularly those based on deep learning (DL), which have shown superior performance compared to other techniques. This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. The studies were primarily sourced from four digital research libraries: ACM, Science Direct, Springer, and IEEE Xplore. The article presents a comprehensive review of DL-based PAD systems, examining recent literature on DL-based PAD methods in finger vein, fingerprint, iris, and face detection systems. Through extensive research of the literature, recent algorithms and their solutions for relevant PAD approaches are thoroughly analyzed. Additionally, the article provides a performance analysis and highlights the most promising research findings. The discussion section addresses current issues, opportunities for advancement, and potential solutions associated with deep learning-based PAD methods. This study is valuable to various community users seeking to understand the significance of this technology and its recent applicability in the development of biometric technology for deep learning.

Mitigating Sensor and Acquisition Method-Dependence of Fingerprint Presentation Attack Detection Systems by Exploiting Data from Multiple Devices

The problem of interoperability is still open in fingerprint presentation attack detection (PAD) systems. This involves costs for designers and manufacturers who intend to change sensors of personal recognition systems or design multi-sensor systems, because they need to obtain sensor-specific spoofs and retrain the system. The solutions proposed in the state of the art to mitigate the problem still require data from the target sensor and are therefore not exempt from the problem of obtaining new data. In this paper, we provide insights for the design of PAD systems thanks to an overview of an interoperability analysis on modern systems: hand-crafted, deep-learning-based, and hybrid. We investigated realistic use cases to determine the pros and cons of training with data from multiple sensors compared to training with single sensor data, and drafted the main guidelines to follow for deciding the most convenient PAD design technique depending on the intended use of the fingerprint identification/authentication system.

Towards realistic fingerprint presentation attacks: The ScreenSpoof method

Using mobile devices, people leave latent fingerprints on the screen that can be photographed and used as a mold to fabricate high-quality spoofs. In this paper, we analysed the threat level of realistic attacks using snapshot pictures of latent fingerprints against presentation attack detection systems. Our evaluation compares a semi-consensual acquisition, given by a voluntary pressure of the finger on the screen, with a completely non-consensual one, given by the normal use of the device.

Domain-Specific Adaptation of CNN for Detecting Face Presentation Attacks in NIR

For the automotive industry moving towards personalized applications and experiences, the identification of the person inside vehicle is necessary; and it must be carried out in a secure manner. In this paper, we propose a unique face presentation attack detection (PAD) system for operation inside a passenger vehicle. A typical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vehicular</i> face PAD system is required to function with several constraints such as bounded sensing (imaging) capabilities, limited computing resources on embedded devices, real-time inference, and essentially, very high accuracy. In this work, we develop a face PAD system for automotive domain, relying on a single NIR camera, to continually verify whether the driver’s face is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bona-fide</i> or not. Our work has two main contributions: first, a lightweight face PAD framework has been developed using a 9-layer convolutional neural network (CNN). With its compact size and limited set of operators, it can be deployed in a resource constrained embedded device to achieve a near real-time inference. To alleviate the problem of limited training data (face PAD in NIR) for a given system, we develop an efficient mechanism to obtain this CNN through the combination of adaptation of domain-specific layers and task-specific fine-tuning of a base CNN. As the second contribution, we collect a large face PAD dataset with 5800+ videos, acquired in NIR (940 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nm</i> ) illumination, for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-vehicular</i> use-cases. This dataset, named VFPAD, captures several real-world variations in terms of environmental settings, illumination, subject’s pose, and appearances. Based on the VFPAD dataset, we demonstrate that the proposed face PAD method achieves very high performance (overall accuracy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 98.0$ </tex-math></inline-formula> %), and also outperforms several baseline face PAD methods. The dataset will be shared with the wider scientific community for research purposes.

Vulnerabilities of Unattended Face Verification Systems to Facial Components-based Presentation Attacks: An Empirical Study

As face presentation attacks (PAs) are realistic threats for unattended face verification systems, face presentation attack detection (PAD) has been intensively investigated in past years, and the recent advances in face PAD have significantly reduced the success rate of such attacks. In this article, an empirical study on a novel and effective face impostor PA is made. In the proposed PA, a facial artifact is created by using the most vulnerable facial components, which are optimally selected based on the vulnerability analysis of different facial components to impostor PAs. An attacker can launch a face PA by presenting a facial artifact on his or her own real face. With a collected PA database containing various types of artifacts and presentation attack instruments (PAIs), the experimental results and analysis show that the proposed PA poses a more serious threat to face verification and PAD systems compared with the print, replay, and mask PAs. Moreover, the generalization ability of the proposed PA and the vulnerability analysis with regard to commercial systems are also investigated by evaluating unknown face verification and real-world PAD systems. It provides a new paradigm for the study of face PAs.

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

IriTrack

With a growing adoption of face authentication systems in various application scenarios, face Presentation Attack Detection (PAD) has become of great importance to withstand artefacts. Existing methods of face PAD generally focus on designing intelligent classifiers or customized hardware to differentiate between the image or video samples of a real legitimate user and the imitated ones. Although effective, they can be resource-consuming and suffer from performance degradation due to environmental changes. In this paper, we propose IriTrack, which is a simple and efficient PAD system that takes iris movement as a significant evidence to identify face artefacts. More concretely, users are required to move their eyes along with a randomly generated poly-line, where the resulting trajectories of their irises are used as an evidence for PAD i.e., a presentation attack will be identified if the deviation of one's actual iris trajectory from the given poly-line exceeds a threshold. The threshold is carefully selected to balance the latency and accuracy of PAD. We have implemented a prototype and conducted extensive experiments to evaluate the performance of the proposed system. The results show that IriTrack can defend against artefacts with moderate time and memory overheads.

Presentation attack detection system for fake Iris: a review

The real-time solicitations of biometric systems have been extensively used for several things with the growing necessities of higher security level. There are numerous biometric traits used for person identification. In recent years, iris biometric trait become very popular and efficient in many security applications. However, biometric systems are prone to presentation attack. This attack is carried out by using spoofing of any biometric modality and present as a real trait. The objective of this paper is to present a broad and well thought-out overview of the effort that has been conceded out over the preceding years in the field of iris anti-spoofing. Spoofing of an iris image can be done by paper print and contact lens etc. So, it is essential to discover fake iris up to its core level. In this survey, we have discussed various research work done in the past on the topic of different classes of presentation attack and summarized the state-of-the-art methods in a structural way. We have also discussed the future direction in the field of iris liveness detection.

Integrated Replay Spoofing-Aware Text-Independent Speaker Verification

A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings, while presentation attack detection systems exploit such information. Therefore, we propose a back-end modular approach using a separate deep neural network (DNN) for speaker verification and presentation attack detection. This approach has thee input components: two speaker embeddings (for enrollment and test each) and prediction of presentation attacks. Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection. The proposed back-end approach demonstrates a relative improvement of 21.77% in terms of the equal error rate for integrated trials compared to a conventional speaker verification system.

Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks

Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in generalizing to unseen attacks. In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN). A novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks. A one-class Gaussian Mixture Model is used on top of these embeddings for the PAD task. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes. This is particularly important as collecting bonafide data and simpler attacks are much easier than collecting a wide variety of expensive attacks. The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks. Further, we have performed experiments with MLFP and SiW-M datasets using RGB channels only. Superior performance in unseen attack protocols shows the effectiveness of the proposed approach. Software, data, and protocols to reproduce the results are made available publicly.

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network.

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

A review on presentation attack detection system for fake fingerprint

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

Long-Term Spectral Statistics for Voice Presentation Attack Detection

Automatic speaker verification systems can be spoofed through recorded, synthetic, or voice converted speech of target speakers. To make these systems practically viable, the detection of such attacks, referred to as presentation attacks, is of paramount interest. In that direction, this paper investigates two aspects: 1) a novel approach to detect presentation attacks where, unlike conventional approaches, no speech signal modeling related assumptions are made, rather the attacks are detected by computing first-order and second-order spectral statistics and feeding them to a classifier, and 2) generalization of the presentation attack detection systems across databases. Our investigations on ASVspoof 2015 challenge database and AVspoof database show that, when compared to the approaches based on conventional short-term spectral features, the proposed approach with a linear discriminative classifier yields a better system, irrespective of whether the spoofed signal is replayed to the microphone or is directly injected into the system software process. Cross-database investigations show that neither the short-term spectral processing-based approaches nor the proposed approach yield systems which are able to generalize across databases or methods of attack. Thus, revealing the difficulty of the problem and the need for further resources and research.

Impact of Score Fusion on Voice Biometrics and Presentation Attack Detection in Cross-Database Evaluations

Research in the area of automatic speaker verification (ASV) has been advanced enough for the industry to start using ASV systems in practical applications. However, these systems are highly vulnerable to spoofing or presentation attacks, limiting their wide deployment. Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing ASV systems for practical and attack-resistant solutions. To be practical, however, an attack detection should (i) have high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient. Several audio-based presentation attack detection (PAD) methods have been proposed recently but their evaluation was usually done on a single, often obscure, database with limited number of attacks. Therefore, in this paper, we conduct an extensive study of eight state-of-the-art PAD methods and evaluate their ability to detect known and unknown attacks (e.g., in a cross-database scenario) using two major publicly available speaker databases with spoofing attacks: AVspoof and ASVspoof. We investigate whether combining several PAD systems via score fusion can improve attack detection accuracy. We also study the impact of fusing PAD systems (via parallel and cascading schemes) with two i-vector and inter-session variability based ASV systems on the overall performance in both bona fide (no attacks) and spoof scenarios. The evaluation results question the efficiency and practicality of the existing PAD systems, especially when comparing results for individual databases and cross-database data. Fusing several PAD systems can lead to a slightly improved performance; however, how to select which systems to fuse remains an open question. Joint ASV-PAD systems show a significantly increased resistance to the attacks at the expense of slightly degraded performance for bona fide scenarios.