Fingerprint Presentation Attack Detection Research Articles

Fingerprint Presentation Attack Detection by Region Decomposition

Fingerprint Presentation Attack Detection by Region Decomposition

An Interpretable Siamese Attention Res‐CNN for Fingerprint Spoofing Detection

In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res‐CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res‐CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross‐material experiments and cross‐sensor experiments. Additionally, we leverage Gradient‐weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation.

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

An encoded histogram of ridge bifurcations and contours for fingerprint presentation attack detection

In recent years, the exponential growth of internet technologies has made personal authentication an integral part of security applications. The fingerprint-based biometric systems are essentially used to safeguard the users' privacy and confidentiality. However, such systems are prone to spoof attacks by artificial replicas of the fingerprints. This paper presents an improved feature extractor called BiRi-PAD (Encoded Histogram of Ridge Bifurcations and Contours for fingerprint Presentation Attack Detection) that aims to enhance the accuracy of live fingerprint detection. The feature extraction process of the proposed BiRi-PAD consists of four steps. First, the fingerprint image undergoes a process of extracting 2-channel ridge contour maps (2-RC maps). The first channel of 2-RC maps consists of ridge contours extracted by a set of derivative filters in the spatial domain whereas the second channel of 2-RC maps consists of the ridge contours extracted by the maximum moments based on phase congruency in the frequency domain. Further, minutiae-based feature information i.e., ridge bifurcations are extracted by the minimum moments based on phase congruency covariance. Moreover, a fusion equation is proposed to integrate 2-RC maps and bifurcations into a single feature map. Second, an improved Comprehensive Local Phase Quantization (CLPQ) based on the well-known feature descriptor Rotation Invariant Local Phase Quantization (LPQri) is proposed to extract the phase information of ridges. CLPQ extracts the orientation of the ridges by using the complex parts of the significant frequency components of LPQri and monogenic filters. Third, the proposed BiRi-PAD quantizes the 2-RC maps into pre-determined intervals. Finally, both 2-RC maps and CLPQ features are integrated to generate a feature vector of a single fingerprint image. Performance evaluations of BiRi-PAD are conducted on three publicly available benchmarks from the LivDet competition, namely LivDet 2013, 2011, and 2015. Experimental evaluations demonstrate that the proposed BiRi-PAD achieves significant reductions in average rates compared to state-of-the-art techniques of fingerprint liveness detection. Specifically, on LivDet 2013, LivDet 2011, and LivDet 2015, the average rates are reduced to 1.92%, 4.39%, and 4.55%, respectively.

An Enhanced Generative Adversarial Network Model for Fingerprint Presentation Attack Detection

An Enhanced Generative Adversarial Network Model for Fingerprint Presentation Attack Detection

SFincBuster: Spoofed fingerprint buster via incremental learning using leverage bagging classifier

The fingerprint presentation attack detection (PAD) studies are extensively explored by investigators to augment the security aspects of human authentication in biometric systems. Although existing approaches yield promising results while evaluated on data having same distribution as the training data, but their performance is declined in scenarios when data is gathered from unknown environment. In these situations model is learned with the new type of fake samples by re-training the entire model. Nevertheless, it has been a non-trivial concern to re-train the entire system to tackle the newly created fake samples as unseen artifacts are generated continuously in real-time scenarios. In this work, we expound a novel incremental learning-based approach namely; SFincBuster that can work effectively in the challenging real-time scenarios and can handle both new data as well as new observations from old classes. We train a leveraging bagging ensemble (LBE) in incremental fashion regardless of the extracted deep-level features (using pre-trained VGG19 network) being too large to accommodate into system memory, it is still possible to train our model effectively. Furthermore, the LBE integrates the simplicity of classical bagging with augmented randomization to the input and outcome of the base classifiers. To tackle the issue of change in distribution that arises with gradual changes from learned fakes to entirely new fingerprint artifacts, the SFincBuster employs LBE with ADWIN (Adaptive WINDowing) technique that continuously evaluate the performance of underlying base model and whenever a change is detected the weakest classifier is substituted with a new one. Our approach achieves high classification accuracy, even though it is not prerequisite to access all features at once. The SFincBuster is trained and evaluated on LivDet 2009, LivDet 2011, LivDet 2013, LivDet 2015 and LivDet 2021 benchmark datasets and yields maximum average classification accuracy (ACA) of 98.65% on LivDet 2013 and LivDet 2015 datasets. The model exhibits stupendous generalization capabilities with an average classification error rate (ACER) of 1.39% for known fakes (KF) and 2.84% for unknown fakes (UF). Finally, the comparable investigation perceives that the SFincBuster model demonstrates a noteworthy performance gain over the similar state-of-the-art (SOTA) approaches and achieve an improved benchmark for real-time cross-sensor, cross-material and cross-dataset scenarios.

Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection

AbstractThe widespread use of fingerprint authentication systems (FASs) in consumer electronics opens for the development of advanced presentation attacks, that is, procedures designed to bypass a FAS using a forged fingerprint. As a consequence, FAS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognise live fingerprints from fake replicas. In this work, a novel FPAD approach based on Convolutional Neural Networks (CNNs) and on an ad hoc adversarial data augmentation strategy designed to iteratively increase the considered detector robustness is proposed. In particular, the concept of adversarial fingerprint, that is, fake fingerprints disguised by using ad hoc fingerprint adversarial perturbation algorithms was leveraged to help the detector focus only on salient portions of the fingerprints. The procedure can be adapted to different CNNs, adversarial fingerprint algorithms and fingerprint scanners, making the proposed approach versatile and easily customisable todifferent working scenarios. To test the effectiveness of the proposed approach, the authors took part in the LivDet 2021 competition, an international challenge gathering experts to compete on fingerprint liveness detection under different scanners and fake replica generation approach, achieving first place out of 23 participants in the ‘Liveness Detection in Action track’.

SpoofGAN: Synthetic Fingerprint Spoof Images

A major limitation to advances in fingerprint presentation attack detection (PAD) is the lack of publicly available, large-scale datasets, a problem which has been compounded by increased concerns surrounding privacy and security of biometric data. Furthermore, most state-of-the-art PAD algorithms rely on deep networks which perform best in the presence of a large amount of training data. This work aims to demonstrate the utility of synthetic (both bona fide and PA style) fingerprints in supplying these algorithms with sufficient data to improve the performance of fingerprint PAD algorithms beyond the capabilities when training on a limited amount of publicly available “real” datasets. First, we provide details of our approach in modifying a state-of-the-art generative architecture to synthesize high quality bona fide and PA fingerprints. Then, we provide quantitative and qualitative analysis to verify the quality of our synthetic fingerprints in mimicking the distribution of real data samples. We showcase the utility of our synthetic bona fide and PA fingerprints in training a deep network for fingerprint PAD, which dramatically boosts the performance across three different evaluation datasets compared to an identical model trained on real data alone. Finally, we demonstrate that only 25% of the original (real) dataset is required to obtain similar detection performance when augmenting the training dataset with synthetic data. We make our synthetic dataset and model publicly available to encourage further research on this topic: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/groszste/SpoofGAN</uri> .

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.

An Effective Orchestration for Fingerprint Presentation Attack Detection

Fingerprint presentation attack detection has become significant due to a wide-spread usage of fingerprint authentication systems. Well-replicated fingerprints easily spoof the authentication systems because their captured images do not differ from those of genuine fingerprints in general. While a number of techniques have focused on fingerprint presentation attack detection, they suffer from inaccuracy in determining the liveness of fingerprints and performance degradation on unknown types of fingerprints. To address existing limitations, we present a robust fingerprint presentation attack detection method that orchestrates different types of neural networks by incorporating a triangular normalization method. Our method has been evaluated on a public benchmark comprising 13,000 images with five different fake materials. The evaluation exhibited our method’s higher accuracy in determining the liveness of fingerprints as well as better generalization performance on different types of fingerprints compared to existing techniques.

Fingerprint Presentation Attack Detection by Channel-Wise Feature Denoising

Due to the diversity of attack materials, fingerprint recognition systems (AFRSs) are vulnerable to malicious attacks. It is thus important to propose effective fingerprint presentation attack detection (PAD) methods for the safety and reliability of AFRSs. However, current PAD methods often exhibit poor robustness under new attack types settings. This paper thus proposes a novel channel-wise feature denoising fingerprint PAD (CFD-PAD) method by handling the redundant noise information ignored in previous studies. The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels. Then, the propagation of "noise" channels is suppressed in the feature map to reduce interference. Specifically, a PA-Adaptation loss is designed to constrain the feature distribution to make the feature distribution of live fingerprints more aggregate and that of spoof fingerprints more disperse. Experimental results evaluated on the LivDet 2017 dataset showed that the proposed CFD-PAD can achieve a 2.53% average classification error (ACE) and a 93.83% true detection rate when the false detection rate equals 1.0% (TDR@FDR=1%). Also, the proposed method markedly outperforms the best single-model-based methods in terms of ACE (2.53% vs. 4.56%) and TDR@FDR=1%(93.83% vs. 73.32%), which demonstrates its effectiveness. Although we have achieved a comparable result with the state-of-the-art multiple-model-based methods, there still is an increase in TDR@FDR=1% from 91.19% to 93.83%. In addition, the proposed model is simpler, lighter and more efficient and has achieved a 74.76% reduction in computation time compared with the state-of-the-art multiple-model-based method. The source code is available at https://github.com/kongzhecn/cfd-pad.

FinPAD: State-of-the-art of fingerprint presentation attack detection mechanisms, taxonomy and future perspectives

In the recent times, with an increase in human identity thefts, the fingerprint-based biometric systems play a significant role in secured authentication and access restrictions. The security and privacy are key aspects that need peculiar reflection while designing these recognition systems. However, imposters practice several presentation attack instruments (PAIs) to exploit the biometrical infrastructure to breach the security aspects. Though, biometric systems are receptive to diverse threats, but the presentation attacks (PAs) are the most widely attempted in current scenario. In PA, an attacker makes use of an artifact of a real biometric trait in order to circumvent the sensor module of the system. In this article, we expound state-of-the-art fingerprint presentation attack detection (FinPAD) mechanisms along with taxonomy covering the period of 2001–2021. The article presents a comprehensive survey of classical hardware-based, handcrafted fingerprint PAD approaches with a special focus on recent deep leaning-based techniques. We provide a summary of publically available fingerprint anti-spoofing databases, standard PAD evaluation protocols, and fingerprint liveness detection competition (LivDet) series till the year 2021. The study explored several open research challenges that yield future directions to the investigators in this active field of research. Our study reveals that the modern data-driven FinPAD techniques are robust and efficient as compared to their hardware-based counterpart in terms of performance. However, designing lightweight fingerprint PAD techniques with smaller datasets that offer better performance in cross-dataset, cross-material, and cross-sensor scenario still remain an open research issue.

Fingerprint Presentation Attack Detection in Open-Set Scenario Using Transient Liveness Factor

Background: In recent history, fingerprint presentation attack detection (FPAD) proposal came out in a variety of ways. A close-set approach uses pattern classification technique that best suits to a specific context and goal. Openset approach works fine in wider context, which is relatively robust with new fabrication material and independent of sensor type. In both case results were promising but not too generalizable because of unseen condition not fitting into method used. It is clear, the two key challenges in FPAD system, sensor interoperability and robustness with new fabrication materials not addressed to date. Objective: To address above challenge a liveness detection model is proposed using live sample using transient liveness factor and one-class CNN. Methods: In our architecture, liveness is predicted by using the fusion rule, score level fusion of two decisions. Here, ‘n’ high quality live samples are initially trained for quality. We have observed that fingerprint liveness information is ‘transitory’ in nature, a variation in the different live sample is natural. Thus, each live sample has a ‘transient liveness’ (TL) information. We use no-reference (NR) image quality measure (IQM) as a transient value corresponding to each live sample. A consensus agreement is collectively reached in transient value to predict adversarial input. Further, live sample at server are trained with augmented inputs on the one-class classifier to predict the outlier. So, by using the fusion rule, score level fusion of consensus agreement and appropriately characterized negative cases (or outliers) predicts liveness. Results: Our approach uses high quality 30-live sample only, out of 90 images available in dataset to reduce learning time. We used Time Series images from LivDet competition 2015. It has 90-live images and 45-spoof images made from Bodydouble, Ecoflex and Playdoh of each person. Fusion rule results in 100% accuracy in recognising live as live. Conclusion: We have presented an architecture for liveness-server for extraction/updating transient liveness factor. Our work explained here a significant step forward towards generalized and reproducible process with a consideration towards the provision for the universal scheme as a need of today. The proposed TLF approach has a solid presumption; it will address dataset heterogeneity as it incorporates wider scope-context. Similar results with other dataset are under validation. Implementation seems difficult now but have several advantages when carried out during the transformative process.

An intelligent approach for fingerprint presentation attack detection using ensemble learning with improved local image features

An intelligent approach for fingerprint presentation attack detection using ensemble learning with improved local image features

Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection.

With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained from different sensors. Our aim is to build a model that leverages the limited sample issues in all target domains by transferring knowledge from the source domain. To this end, we train a unified generative adversarial network (UGAN) for multidomain conversion to learn several mappings between all domains. This allows us to generate additional synthetic images for the target domains from the source domain to reduce the distribution shift between fingerprint representations. Then, we train a scale compound network (EfficientNetV2) coupled with multiple head classifiers (one classifier for each domain) using the source domain and the translated images. The outputs of these classifiers are then aggregated using an additional fusion layer with learnable weights. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset. The experimental results show that the proposed method improves the average classification accuracy over twelve classification scenarios from 67.80 to 80.44% after adaptation.

Multi-Modal Fingerprint Presentation Attack Detection: Evaluation on a New Dataset

Fingerprint presentation attack detection is becoming an increasingly challenging problem due to the continuous advancement of attack preparation techniques, which generate realistic-looking fake fingerprint presentations. In this work, rather than relying on legacy fingerprint images, which are widely used in the community, we study the usefulness of multiple recently introduced sensing modalities. Our study covers front-illumination imaging using short-wave-infrared, near-infrared, and laser illumination; and back-illumination imaging using near-infrared light. Toward studying the effectiveness of each of these unconventional sensing modalities and their fusion for liveness detection, we conducted a comprehensive analysis using a fully convolutional deep neural network framework. Our evaluation compares different combinations of the new sensing modalities to legacy data from one of our collections, showing the superiority of the new sensing modalities. It also covers the cases of known and unknown attacks and the cases of intra-dataset and inter-dataset evaluations. Our results indicate that the power of our approach stems from the nature of the captured data rather than the employed classification framework, which justifies the extra cost for hardware-based (or hybrid) solutions. The portion of the dataset that is under the control of the authors is publicly released, along with the associated code.

HyFiPAD: a hybrid approach for fingerprint presentation attack detection using local and adaptive image features

HyFiPAD: a hybrid approach for fingerprint presentation attack detection using local and adaptive image features

Modeling Fingerprint Presentation Attack Detection Through Transient Liveness Factor-A Person Specific Approach

A self-learning, secure and independent open-set solution is essential to be explored to characterise the liveness of fingerprint presentation. Fingerprint spoof presentation classified as live (a Type-I error) is a major problem in a high-security establishment. Type-I error are manifestation of small number of spoof sample. We propose to use only live sample to overcome above challenge. We put forward an adaptive ‘fingerprint presentation attack detection’ (FPAD) scheme using interpretation of live sample. It requires initial high-quality live fingerprint sample of the concerned person. It uses six different image quality metrics as a transient attribute from each live sample and record it as ‘Transient Liveness Factor’ (TLF). Our study also proposes to apply fusion rule to validate scheme with three outlier detection algorithms, one-class support vector machine (SVM), isolation forest and local outlier factor. Proposed study got phenomenal accuracy of 100% in terms of spoof detection, which is an open-set method. Further, this study proposes and discuss open issues on person specific spoof detection on cloud-based solutions.