Fingerprint Spoof Research Articles

Performance evaluation and forensic examination of common fingerprint spoofing materials

Performance evaluation and forensic examination of common fingerprint spoofing materials

CLNet: a contactless fingerprint spoof detection using deep neural networks with a transfer learning approach

CLNet: a contactless fingerprint spoof detection using deep neural networks with a transfer learning approach

Using synthetic data to fight fingerprint spoofing

Fime's Joël Di Manno, Jean Fang and Abdarahmane Wone outline the latest research into the threat that spoofs present to fingerprint authentication – and how synthetic data can help counter this risk.

Leveraging Deep Learning to Fingerprint Spoof Detectors: Hitherto and Futuristic Perspectives

Fingerprints being the most widely employed biometric trait, due to their high acceptability and low sensing cost, have replaced the traditional methods of human authentication. Although, the deployment of these biometrics-based recognition systems is accelerating, they are still susceptible to spoofing attacks where an attacker presents a fake artifact generated from silicone, candle wax, gelatin, etc. To safeguard sensor modules from these attacks, there is a requirement of an anti-deception mechanism known as fingerprint spoof detectors (FSD) also known as anti-spoofing mechanisms. A lot of research work has been carried out to design fingerprint anti-spoofing techniques in the past decades and currently, it is oriented towards deep learning (DL)-based modeling. In the field of fingerprint anti-spoofing, since the 2014, the paradigm has shifted from manually crafted features to deep features engineering. Hence, in this study, we present a detailed analysis of the recent developments in DL based FSDs. Additionally, we provide a brief comparative study of standard evaluation protocols that include benchmark anti-spoofing datasets as well as performance evaluation metrics. Although significant progress has been witnessed in the field of DL-based FSDs, still challenges are manifold. Therefore, we investigated these techniques critically to list open research issues along with their viable remedies that may put forward a future direction for the research community. The majority of the research work reveals that deep feature extraction for fingerprint liveness detection demonstrates promising performance in the case of cross-sensor scenarios. Though convolution neural network (CNN) models extract deep-level features to improve the classification accuracy, their increased complexity and training overhead is a tradeoff between both the parameters. Furthermore, enhancing the performance of presentation attack detection (PAD) techniques in the cross-material scenario is still an open challenge for researchers.

Mitigating voltage fingerprint spoofing attacks on the controller area network bus

The Controller Area Network (CAN) bus suffers security vulnerabilities that allow message spoofing and masquerading Electronic Control Units (ECUs). A popular provision for mitigating these vulnerabilities is through the use of machine learning (ML) to derive ECU fingerprints based on the physical properties of bus signals. Particularly, voltage-based intrusion detection systems associate the message transmitter with its voltage fingerprint to detect conflicting logical ECU identifiers in the presence of cyberattacks. However, the signal characteristics depend on the operating conditions and hence the fingerprints need to be adapted overtime by online training of the underlying ML model. An adversary may exploit such a shortcoming to superimpose training data based on its own transmissions and thus bypass the protection mechanism. Such an attack not only allows device impersonation but also leads to rejecting transmissions of a legitimate ECU. This paper proposes an effective approach to thwart these attack scenarios. Our approach introduces unpredictably-scheduled transmissions involving one or multiple ECUs to confuse the adversary and ensure the generation of a legitimate fingerprinting dataset for online training. We validate the robustness of our approach using data collected from a real vehicle and show that it outperforms a prominent competing scheme by over 30% in terms of identifying malicious ECUs when the attacker could overwrite 50% of the retraining transmissions.

Fingerprint Spoofing: Exploring Cybersecurity With Limited Technology

Fingerprint Spoofing: Exploring Cybersecurity With Limited Technology

Fingerprint Liveness Detection Schemes: A Review on Presentation Attack

ABSTRACT Extant literature has highlighted the vulnerability of Automatic Fingerprint Identification System (AFIS) to various forms of attacks, indicating presentation attack as the most predominant. This form of attack involves the malicious utilization of ubiquitous materials such as silicone, gelatin, playdoh, among many others, to fabricate synthetic fingerprints to circumvent AFIS. As a result, various studies have posited some countermeasures, encompassing the use of hardware- based and software-based techniques. The hardware-based methods necessitate the integration of supplementary sensors for capturing other live human traits such as pulse rate, odour etc. Conversely, the software-based methods are focused on feature extraction and deep learning schemes. However, despite the robustness of the software-based approach as compared to the hardware-based technique, both schemes are still faced with an immense challenge in developing fingerprint spoof generalized models to mitigate the concern of cross-material (novel) detection. As a result, various studies have highlighted that the issue of fingerprint spoofing should be treated an” opened-set problem” (training only live fingerprints), rather than a” closed-set problem” (training live & spoof), birthing the development of fingerprint one-class classifiers. This article presents a comprehensive detail on fingerprint spoofing, extant countermeasures and the challenge still faced by fingerprint spoof detectors.

Proactive forensic science in biometrics: Novel materials for fingerprint spoofing.

Motivated by the need to prepare for the next generation of fingerprint spoofing, we applied the “proactive forensic science” strategy to the biometric field. The working concept, already successful in a few fields, aimed at adopting the sophisticated criminals' way of thinking, predicting their next move so that the crime‐fighting authorities can be one step ahead of them and take preventive measures, against biometric spoofing in this instance. This strategy involved the design, production, and characterization of innovative polymeric materials that could possibly serve in advanced fingerprint spoofs. Special attention was given to materials capable of fooling fingerprint readers equipped with spoof‐detecting abilities, known as “Presentation Attack Detection” (PAD) systems and often referred to as liveness detection. A series of direct cast fake fingerprints was produced from known commercially available spoofing materials, and was functionally tested to compare their performance with that of spoofs produced from the new polymers. The novel materials thus prepared were hydrogels based on polyethylene glycols (PEGs) that were chain‐extended. They showed good performance in deceiving security systems, considerably better than that of spoofs produced from commercial materials, and are, therefore, good spoofing candidates that law‐enforcement authorities should be aware of.

Fingerprint Presentation Attack Detection Approaches in Open-Set and Closed-Set Scenario

In the biometric system, fingerprints are widely used to recognise an individual’s identity and are vulnerable to presentation attacks such as spoofing. Spoof fingerprints are fake fingerprints created using artificial materials like play-do, silicone, etc., which fool the biometric identification system. Spoof handling is an urgent need nowadays, as these attacks’ success rate is more than 70%. Much work has been done on fingerprint spoof detection, but mostly involved closed-set problems, i.e. spoof materials used are the same for training and testing set, but they face poor generalisation problem. Hence to overcome closed-set problems, Open-set solution came into existence in which some novel spoof materials were introduced in the testing set, which was not seen during training. This paper will give a brief review of various closed-set and open-set solution techniques along with the tabular view of their Average Classification Error (ACE) scores.

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.

Fingerprint Distortion Detection

Fingerprint is widely used in biometrics, for identification of individual’s identity. Biometric recognition is a leading technology for identification and security systems. It has unique identification among all other biometric modalities. Most anomaly detection systems rely upon machine learning. Calculations are performed to identify suspicious occasion. The primary purpose of this system is to ensure a reliable and accurate user authentication; this study addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. The approach is to utilize local patches centered and aligned using fingerprint details. That proposed approach is to provide accuracies in fingerprint spoof detection for intra-sensor, cross material, crosssensor, as well as cross-dataset testing scenarios. The principle used is similar to the working of some cryptographic primitives, in particular to present the key into the plan so that a couple of operations are infeasible without knowing it.

Towards Fingerprint Spoofing Detection in the Terahertz Range.

Spoofing attacks using imitations of fingerprints of legal users constitute a serious threat. In this study, a terahertz time domain spectroscopy (TDS) setup in a reflection configuration was used for the non-intrusive detection of fingerprint spoofing. Herein, the skin structure of the finger pad is described with a focus on the outermost stratum corneum. We identified and characterized five representative spoofing materials and prepared thin and thick finger imitations. The complex refractive index of the materials was determined in TDS in the transmission configuration. For dataset collection, we selected a group of 16 adults of various ages and genders. The reflection results were analyzed both in the time (reflected signal) and frequency (reflectivity) domains. The measured signals were positively verified with the theoretical calculations. The signals corresponding to samples differ from the finger-related signals, which facilitates spoofing detection. Thanks to deconvolution, we provide a basic explanation of the observed phenomena. We propose two spoofing detection methods, predefined time–frequency features and deep learning based. The methods achieved high true detection rates of 87.9% and 98.8%. Our results show that the terahertz technology can be successfully applied for spoofing detection with high detection probability.

Fingerprint Spoof Detector Generalization

We present a style-transfer based wrapper, called Universal Material Generator (UMG), to improve the generalization performance of any fingerprint spoof (presentation attack) detector against spoofs made from materials not seen during training. Specifically, we transfer the style (texture) characteristics between fingerprint images of known materials with the goal of synthesizing fingerprint images corresponding to unknown materials, that may occupy the space between the known materials in the deep feature space. Synthetic live fingerprint images are also added to the training dataset to supervise the CNN to learn generative-noise invariant features which discriminate between lives and spoofs. The proposed approach is shown to improve the generalization performance of two state-of-the-art spoof detectors, namely Fingerprint Spoof Buster and Slim-ResCNN, winner of the LivDet 2017 spoof detection competition. Specifically, the performance is improved from TDR of 75.24% and 73.09% to TDR of 91.78% and 90.63% @ FDR = 0.2% for Spoof Buster and Slim-ResCNN, respectively. These results are based on a large-scale dataset of 5,743 live and 4,912 spoof images fabricated using 12 different materials. In addition to generalization across different spoof materials, the proposed approach is also shown to improve the average cross-sensor spoof detection performance from 67.60% and 64.62% to 80.63% and 77.59%, for Fingerprint Spoof Buster and Slim-ResCNN, respectively, when tested on the LivDet 2017 dataset.

Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning

Online banking and financial services using mobile applications are seeing a persistent growth among customers, who are using these for their financial transactions. This rise in the use of such applications in smart devices has increased security concerns. There is need for secure mechanisms to prevent fraud and protect personal information. This paper investigates the use of biometric identification in banking and financial services, which leverage the use of smartphones and tablets. While customer engagement and brand loyalty are important concerns, these services are making use of biometric authentication to make customer interactions more secure. However, as technology is growing rapidly, spoofing attacks are becoming common. In this paper, authors have proposed a robust framework to detect spoofing attacks in fingerprint recognition. The process of spoofing detection involves contrast enhancement using histogram equalization and a deep convolutional neural network architecture. Authors have validated the results on various biometric spoofing benchmarks, each one containing real and spoofed samples of user fingerprints. The results indicate that our proposed framework performs better as evaluated against other existing pre-trained CNN models and state-of-the-art methods.

Fingerprint Spoof Buster: Use of Minutiae-Centered Patches

The primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, but the security of the recognition system itself can be jeopardized by spoof attacks. This paper addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. Specifically, we propose a deep convolutional neural network-based approach utilizing local patches centered and aligned using fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides the state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, in LivDet 2015, the proposed approach achieves 99.03% average accuracy over all sensors compared with 95.51% achieved by the LivDet 2015 competition winners. In addition, two new fingerprint presentation attack datasets containing more than 20,000 images, using two different fingerprint readers, and over 12 different spoof fabrication materials are collected. We also present a graphical user interface, called Fingerprint Spoof Buster, that allows the operator to visually examine the local regions of the fingerprint highlighted as live or spoof, instead of relying on only a single score as output by the traditional approaches.

Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Abstract Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.

RaspiReader: Open Source Fingerprint Reader.

We open source an easy to assemble, spoof resistant, high resolution, optical fingerprint reader, called RaspiReader, using ubiquitous components. By using our open source STL files and software, RaspiReader can be built in under one hour for only US $175. As such, RaspiReader provides the fingerprint research community a seamless and simple method for quickly prototyping new ideas involving fingerprint reader hardware. In particular, we posit that this open source fingerprint reader will facilitate the exploration of novel fingerprint spoof detection techniques involving both hardware and software. We demonstrate one such spoof detection technique by specially customizing RaspiReader with two cameras for fingerprint image acquisition. One camera provides high contrast, frustrated total internal reflection (FTIR) fingerprint images, and the other outputs direct images of the finger in contact with the platen. Using both of these image streams, we extract complementary information which, when fused together and used for spoof detection, results in marked performance improvement over previous methods relying only on grayscale FTIR images provided by COTS optical readers. Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.

Liveness Detection and Automatic Template Updating Using Fusion of ECG and Fingerprint

Fingerprints have been extensively used for biometric recognition around the world. However, fingerprints are not secrets, and an adversary can synthesis a fake finger to spoof the biometric system. The mainstream of the current fingerprint spoof detection methods are basically binary classifier trained on some real and fake samples. While they perform well on detecting fake samples created by using the same methods used for training, their performance degrades when encountering fake samples created by a novel spoofing method. In this paper, we approach the problem from a different perspective by incorporating electrocardiogram (ECG). Compared with the conventional biometrics, stealing someone’s ECG is far more difficult if not impossible. Considering that ECG is a vital signal and motivated by its inherent liveness, we propose to combine it with a fingerprint liveness detection algorithm. The combination is natural as both ECG and fingerprints can be captured from fingertips. In the proposed framework, the ECG and fingerprint are combined not only for authentication purpose but also for liveness detection. We also examine automatic template updating using ECG and fingerprint. In addition, we propose a stopping criterion that reduces the average waiting time for signal acquisition. We have performed extensive experiments on the LivDet2015 database which is presently the latest available liveness detection database and compare the proposed method with six liveness detection methods as well as 12 participants of LivDet2015 competition. The proposed system has achieved a liveness detection equal error rate (EER) of 4.2% incorporating only 5 s of ECG. By extending the recording time to 30 s, liveness detection EER reduces to 2.6% which is about 4 times better than the best of six comparison methods. This is also about 2 times better than the best results achieved by the participants of the LivDet2015 competition.

Fingerprint Spoofing Detection using HOG and Local Binary Pattern

Fingerprint Spoofing Detection using HOG and Local Binary Pattern

Contrast Enhanced Subsurface Fingerprint Detection Using High-Speed Optical Coherence Tomography

Spectral-domain optical coherence tomography (SD-OCT) has been demonstrated to be a viable tool in forensic science for fingerprint detection, yet it still suffers from certain practical issues, e.g., the limited scanning speed and low image contrast. In this letter, we report a high-speed SD-OCT together with an image contrast enhancement mechanism for reliable subsurface fingerprint detection. The constructed SD-OCT system achieves a scanning rate up to 60k A-lines/s, and thus, both 3D volumetric images, reaching up to 20 mm $\times 20$ mm $\times 1.2$ mm, and en face internal fingerprint furrow pattern images could be obtained. Based upon the analyses of the papillary layer anatomical structures, the contrast enhancement technique not only suppresses image artifacts, but also is effective in detecting fingerprint spoofing. Experiments on healthy subjects have also been conducted to verify the system imaging capability and the effectiveness of the contrast-enhancement technique.