Presentation Attack Detection Research Articles

T-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators.

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric verification, they address different detection tasks and are hence typically evaluated separately. Evidence shows that this approach is suboptimal. We introduce a new metric for the joint evaluation of PAD solutions operating in situ with biometric verification. In contrast to the tandem detection cost function proposed recently, the new tandem equal error rate (t-EER) is parameter free. The combination of two classifiers nonetheless leads to a set of operating points at which false alarm and miss rates are equal and also dependent upon the prevalence of attacks. We therefore introduce the concurrent t-EER, a unique operating point which is invariable to the prevalence of attacks. Using both modality (and even application) agnostic simulated scores, as well as real scores for a voice biometrics application, we demonstrate application of the t-EER to a wide range of biometric system evaluations under attack. The proposed approach is a strong candidate metric for the tandem evaluation of PAD systems and biometric comparators.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.

Learning Joint Local-Global Iris Representations via Spatial Calibration for Generalized Presentation Attack Detection

Learning Joint Local-Global Iris Representations via Spatial Calibration for Generalized Presentation Attack Detection

Detection of Adversarial Facial Accessory Presentation Attacks Using Local Face Differential

To counter adversarial facial accessory presentation attacks (PAs), a detection method based on local face differential is proposed in this article. It extracts the local face differential features from a suspected face image and a reference face image, and then adaptively fuses the differential features of different local face regions to detect adversarial facial accessory PAs. Meanwhile, the principle of the proposed method is explained by theoretically investigating the local facial differences between a bona fide presentation and an adversarial facial accessory PA when they are compared with a reference face image. To evaluate the proposed method, this article builds a database with different adversarial examples (AEs), presentation attack instruments (PAIs), illumination conditions, and cameras. The experimental results show that it can effectively distinguish between adversarial facial accessory PAs and bona fide presentations, and it has good generalization ability to unseen AEs, PAIs, illumination conditions, and cameras. Moreover, it outperforms the existing AE detection and presentation attack detection methods in detecting adversarial facial accessory PAs.

Multi-Domain Incremental Learning for Face Presentation Attack Detection

Previous face Presentation Attack Detection (PAD) methods aim to improve the effectiveness of cross-domain tasks. However, in real-world scenarios, the original training data of the pre-trained model is not available due to data privacy or other reasons. Under these constraints, general methods for fine-tuning single-target domain data may lose previously learned knowledge, leading to a catastrophic forgetting problem. To address these issues, we propose a multi-domain incremental learning (MDIL) method for PAD, which not only learns knowledge well from the new domain but also maintains the performance of previous domains stably. Specifically, we propose an adaptive domain-specific experts (ADE) framework based on the vision transformer to preserve the discriminability of previous domains. Furthermore, an asymmetric classifier is designed to keep the output distribution of different classifiers consistent, thereby improving the generalization ability. Extensive experiments show that our proposed method achieves state-of-the-art performance compared to prior methods of incremental learning. Excitingly, under more stringent setting conditions, our method approximates or even outperforms the DA/DG-based methods.

A distortion model guided adversarial surrogate for recaptured document detection

Due to the ever-growing need for e-business, document presentation attack detection (DPAD) is an important forensic task. With deep learning, the performances of DPAD methods have been improved significantly. However, the cross-domain performance under different types of document images is not yet satisfactory. In this work, we propose to study the document recapturing distortion model (DM), which is employed in guiding the training of an end-to-end surrogate distortion model. This surrogate model is incorporated into the DPAD scheme in an adversarial training fashion, yielding the proposed Adversarial Surrogate-based DPAD (AS-DPAD) scheme. The surrogate model actively generates adversarial samples with recapturing distortions that confuse the DPAD model. Meanwhile, the DPAD backbone learns a latent space that considers the distances between the generated and real recaptured samples to achieve a better generalization performance. A challenging cross-domain protocol is conducted in our experiment. The results confirm that our DM improves the efficacy of the trained surrogate distortion model and also validate that the proposed adversarial training strategy leads to a significant performance gain in the evaluation of document images with different contents.

MF 2 ShrT: Multimodal Feature Fusion Using Shared Layered Transformer for Face Anti-spoofing

In recent times, Face Anti-spoofing (FAS) has gained significant attention in both academic and industrial domains. Although various convolutional neural network (CNN)-based solutions have emerged, multimodal approaches incorporating RGB, depth, and information retrieval (IR) have exhibited better performance than unimodal classifiers. The increasing veracity of modern presentation attack instruments results in a persistent need to enhance the performance of such models. Recently, self-attention-based vision transformers (ViT) have become a popular choice in this field. Their fundamental aspects for multimodal FAS have not been thoroughly explored yet. Therefore, we propose a novel framework for FAS called MF 2 ShrT, which is based on a pretrained vision transformer. The proposed framework uses overlap patches and parameter sharing in the ViT network, allowing it to utilize multiple modalities in a computationally efficient manner. Furthermore, to effectively fuse intermediate features from different encoders of each ViT, we explore a T-encoder-based hybrid feature block enabling the system to identify correlations and dependencies across different modalities. MF 2 ShrT outperforms conventional vision transformers and achieves state-of-the-art performance on benchmarks CASIA-SURF and WMCA, demonstrating the efficiency of transformer-based models for presentation attack detection PAD).

An Adaptive Spatio-temporal Global Sampling for Presentation Attack Detection

Without developing dedicated countermeasures, facial biometric systems can be spoofed with printed photos, replay attacks, silicone masks, or even a 3D mask of a targeted person. Thus, the threat of presentation attacks needs to be addressed to strengthen the security of the biometric systems. Since a 2D convolutional neural network (CNN) captures static features from video frames, the camera motion might hinders the performance of modern CNNs for video-based presentation attack detection (PAD). Inspired by the egomotion theory, we introduce an adaptive spatiotemporal global sampling (ASGS) technique to compensate the camera motion and use the resulting estimation to encode the appearance and dynamics of the video sequences into a single RGB image. This is achieved by adaptively splitting the video into small segments and capturing their global motion within each segment. The proposed global motion is estimated based on four key steps: dense sampling, FREAK feature extraction and matching, similarity transformation, and aggregation function. This allows using deep models pre-trained on images for video-based PAD detection. Moreover, the interpretation of ASGS reveals that the most important parts for supporting the decision on PAD are consistent with motion cues associated with the artifacts, i.e., hand movement, material reflection, and expression changes. Extensive experiments on four standard face PAD databases demonstrate its effectiveness and encourage further study in this domain.

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing.

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.

Fingerprint Presentation Attack Detection by Region Decomposition

Fingerprint Presentation Attack Detection by Region Decomposition

Federated Generalized Face Presentation Attack Detection.

Face presentation attack detection (fPAD) plays a critical role in the modern face recognition pipeline. An fPAD model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (both real face images and spoof images) are not directly shared between data owners due to legal and privacy issues. In this article, with the motivation of circumventing this challenge, we propose a federated face presentation attack detection (FedPAD) framework that simultaneously takes advantage of rich fPAD information available at different data owners while preserving data privacy. In the proposed framework, each data owner (referred to as data centers) locally trains its own fPAD model. A server learns a global fPAD model by iteratively aggregating model updates from all data centers without accessing private data in each of them. Once the learned global model converges, it is used for fPAD inference. To equip the aggregated fPAD model in the server with better generalization ability to unseen attacks from users, following the basic idea of FedPAD, we further propose a federated generalized face presentation attack detection (FedGPAD) framework. A federated domain disentanglement strategy is introduced in FedGPAD, which treats each data center as one domain and decomposes the fPAD model into domain-invariant and domain-specific parts in each data center. Two parts disentangle the domain-invariant and domain-specific features from images in each local data center. A server learns a global fPAD model by only aggregating domain-invariant parts of the fPAD models from data centers, and thus, a more generalized fPAD model can be aggregated in server. We introduce the experimental setting to evaluate the proposed FedPAD and FedGPAD frameworks and carry out extensive experiments to provide various insights about federated learning for fPAD.

Iris presentation attack detection: Research trends, challenges, and future directions

<p>Currently, interest in biometrics has increased, and personal identity verification is ubiquitous. Iris recognition techniques have recently attracted considerable attention from researchers and are considered one of the most popular topics as they are used for verification purposes. Because of the increasing use of iris recognition, many potential risks have emerged as a natural result of the increased deployment of these technologies. One of the most serious risks is the so-called presentation attack (PA). A PA is the presentation of a sample to an iris sensor to trick the biometric system into making an incorrect decision. Iris presentation attacks are used to spoof or disguise a person’s identity. Many studies have focused on iris presentation attack detection techniques, which are a subset biometric recognition. However, some gaps remain unsolved, and new challenges are rapidly emerging. Despite significant advances in the literature, the problems in iris presentation attack detection have not been adequately addressed and remain open questions. This paper provides a comprehensive overview of iris presentation attack detection from various aspects (e.g., detection techniques, attack types, datasets, and performance measurements). It also attempts to explore the main challenges that may affect presentation attack detection models in terms of important aspects. The challenges that remain to be unresolved are summarised to facilitate problem solving. This review concludes with some directions for future research to help researchers focus on important aspects of the field and try to improve what previous researchers have started. Furthermore, it is likely that this review will be used as a reference for scientists/researchers in the existing science of iris presentation attack detection.</p>

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.

3sXcsNet: A framework for face presentation attack detection using deep learning

Face Recognition is a widely used authentication method deployed for accessing personal digital devices like mobiles and laptops, digital door locks, or various security applications. However, presentation attacks in form of print, video and mask can circumvent such authentication methods. Several solutions based on texture features are proposed in the state-of-the-art; nevertheless, they are sensitive to varying illumination conditions and are less resilient against 2D and 3D attacks. In this paper, we propose a novel 3-stream compact Xception framework (3sXcsNet) consisting of four input streams RGB, MSRCR, MSRCP, and a depth map to form two specific 3-stream fusion (CRF and CPF) to mitigate the illumination scenarios. These streams provide discriminative features. RGB images have rich texture features; MSRCR and MSRCP images are illumination invariant and preserve better chromaticity applied on a color or intensity channel, respectively. In addition, an auxiliary depth map that provides depth information for each pixel makes it easier to analyze features and recognize whether the input image is from a real person or a spoof medium. The 3-stream CRF and CPF represent the features differently and feed independently to a deep CNN compact Xception with a CBAM network. These feature maps are fused using an average fusion mechanism for classification of real and spoof faces. We utilize both 2D and 3D attack datasets namely, REPLAY-ATTACK, CASIA-FASD, OULU-NPU, and 3DMAD to evaluate the effectiveness of the proposed framework. We attain an EER of 1.27% and 0.19% for CRF fusion, as well as 1.48% and 0.20% for CPF fusion when assessing performance on the CASIA-FASD and REPLAY-ATTACK datasets, respectively. On the OULU-NPU dataset, we obtain the most favorable results for two protocols while employing CRF fusion, resulting in ACER metrics of 0.7% and 3.2±2.7%, respectively. On the other hand, CPF fusion outperformed with an ACER of 1.8±1.0% for one protocol. Our findings on intra-database outperform the current state-of-the-art. We also perform cross-database testing to assess the generalization capability of the framework on 2D and 3D attack datasets. Furthermore, the proposed method outperforms both fusion procedures in terms of error rate HTER for intra-database and cross-database testing on the 3DMAD dataset.

Spoofing Attacks and Anti-Spoofing Methods for Face Authentication Over Smartphones

The ubiquitous usages of smartphones demand reliable mobile authentication systems, which are expected to play an indispensable role in protecting users' sensitive and private information. Among various mobile authentication mechanisms, face authentication (FA) has become the most popular choice on modern smartphones, owing to its excellent performance and user experience. Unfortunately, emerging advanced attacks have brought in severe security concerns against FA systems (e.g., privacy leakage, property damage), which have also triggered increased attention on face anti-spoofing designs. This paper provides a concise review of the advanced face anti-spoofing systems over smartphones from a practical perspective. We first review the presentation attacks (PAs) toward FA systems and classify them according to the geometry and dynamic property of the presentation mediums. Then we survey the state-ofthe- art presentation attack detection (PAD) approaches over smartphones and categorize these methods into four categories based on the cues they leverage. Furthermore, we present a comprehensive comparison of their advantages and disadvantages from various practical aspects. Finally, we provide recommendations for future directions. We hope our viewpoints would shed light on potential pathways for more reliable FA in the future.

Improving Monocular Facial Presentation-Attack-Detection Robustness with Synthetic Noise Augmentations.

We present a synthetic augmentation approach towards improving monocular face presentation-attack-detection (PAD) robustness to real-world noise additions. Face PAD algorithms secure authentication systems against spoofing attacks, such as pictures, videos, and 2D-inspired masks. Best-in-class PAD methods typically use 3D imagery, but these can be expensive. To reduce application cost, there is a growing field investigating monocular algorithms that detect facial artifacts. These approaches work well in laboratory conditions, but can be sensitive to the imaging environment (e.g., sensor noise, dynamic lighting, etc.). The ideal solution for noise robustness is training under all expected conditions; however, this is time consuming and expensive. Instead, we propose that physics-informed noise-augmentations can pragmatically achieve robustness. Our toolbox contains twelve sensor and lighting effect generators. We demonstrate that our toolbox generates more robust PAD features than popular augmentation methods in noisy test-evaluations. We also observe that the toolbox improves accuracy on clean test data, suggesting that it inherently helps discern spoof artifacts from imaging artifacts. We validate this hypothesis through an ablation study, where we remove liveliness pairs (e.g., live or spoof imagery only for participants) to identify how much real data can be replaced with synthetic augmentations. We demonstrate that using these noise augmentations allows us to achieve better test accuracy while only requiring 30% of participants to be fully imaged under all conditions. These findings indicate that synthetic noise augmentations are a great way to improve PAD, addressing noise robustness while simplifying data collection.

Fairness in face presentation attack detection

Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases.

Internal Structure Attention Network for Fingerprint Presentation Attack Detection From Optical Coherence Tomography

Internal Structure Attention Network for Fingerprint Presentation Attack Detection From Optical Coherence Tomography

A uniform representation model for OCT-based fingerprint presentation attack detection and reconstruction

In current Optical Coherence Tomography (OCT)-based fingerprint recognition systems, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction are treated as two independent branches, resulting in high computation and system building complexity. Therefore, this paper proposes a uniform representation model for simultaneous PAD and subsurface fingerprint reconstruction. A novel semantic segmentation network using attention mechanisms was designed to extract and segment multiple subsurface structures from real finger slices (i.e., B-scans). The latent codes derived from the network are directly used to effectively detect PA because they contain abundant subsurface biological information, which is independent of PA materials and has strong robustness for unknown PAs. Segmented subsurface structures were adopted to reconstruct multiple subsurface 2D fingerprints. Extensive experiments were carried out on an in-house database, which is the largest public OCT-based fingerprint database with 2449 volumes. PAD performance was evaluated by comparing the results of the existing methods with those of other segmentation networks. The proposed uniform representation model can obtain an accuracy (Acc) of 96.63%, which achieves a state-of-the-art performance. The effectiveness of subsurface reconstruction was evaluated from the segmentation and recognition results. In the segmentation experiments, the proposed method achieved the best results with an Intersection of Union (mIOU) of 0.834 and a Pixel Accuracy of 0.937. By comparing the recognition performance on surface 2D fingerprints (e.g., commercial and high-resolution), the lowest results with an Equal Error Rate (EER) of 2.25% by minutiae matching and an EER of 5.42% by pore matching are achieved, which indicates the excellent reconstruction capability of the proposed uniform representation model.

An open patch generator based fingerprint presentation attack detection using generative adversarial network

An open patch generator based fingerprint presentation attack detection using generative adversarial network