Biometric-based recognition has been replacing conventional recognition methods in security systems. Modern electronic devices such as smartphones and online services have been employing biometric systems because of their security, acceptability, and usability. However, the wide deployment of Biometrics raises security concerns including attacks that aim to interfere with a system’s operation. This paper provides a review of potential threats which may affect biometric systems’ security, particularly, Presentation Attack (PA). A general taxonomy of presentation attacks is proposed to cover different biometric modalities considering the attacker’s intention and the presentation instrument. Moreover, Presentation Attack Detection (PAD) mechanisms that aim to eliminate or mitigate those attacks are also taxonomized. The taxonomy analyzes PAD mechanisms wherein the biometric trait pattern is considered to classify PAD methods. A state of the art study has been carried out to investigate PA and PAD for six biological and behavioral modalities.

High Dynamic Range (HDR) image provides higher perceptual quality such that it appears considerably more realistic and attractive for the human observer. Since most of current screens are Low Dynamic Range (LDR) screens, lots of researches have been proposed to design tone mapping algorithms converting the HDR images into a range that is suitable to display these tone mapped images on standard LDR screens. For this purpose, this paper first investigates the pixels distribution in the HDR image through the study of various traditional Histogram Equalization (HE) algorithms which were originally developed to improve the visual quality of LDR images such as the contrast enhancement of the latter. Moreover, a modification of the Histogram Adjustment based Linear to Equalized Quantizer (HALEQ), developed for HDR images, is proposed. Simulation results show that the proposed modification preserves more details than the original version of the algorithm in most parts of the HDR image.

This paper presents a novel mechanism for fingerprint dynamic presentation attack detection. We utilize five spatio-temporal feature extractors to efficiently eliminate and mitigate different presentation attack species. The feature extractors are selected such that the fingerprint ridge/valley pattern is consolidated with the temporal variations within the pattern in fingerprint videos. An SVM classification scheme, with a second degree polynomial kernel, is used in our presentation attack detection subsystem to classify bona fide and attack presentations. The experiment protocol and evaluation are conducted following the ISO/IEC 30107-3:2017 standard. Our proposed approach demonstrates efficient capability of detecting presentation attacks with significantly low BPCER where BPCER is 1.11% for an optical sensor and 3.89% for a thermal sensor at 5% APCER for both.

Given that nowadays we store increasing sensitive data on our mobile devices (documents, photos, bank accounts, etc.), it is crucial to know how secure the protection of the phone really is. Most new smartphones include an embedded fingerprint sensor due to its comfort of use and security. In the last decades, many studies and tests have shown that it is possible to steal a person's fingerprint and reproduce it, with the intention of impersonating them. This has become a bigger problem as the adoption of fingerprint sensor cell phones have become mainstream. Thus, it is important to know how easy it really is for anyone to capture a latent fingerprint. For this work, we searched for the easiest process to steal a latent fingerprint from a phone screen. In 2013, the Chaos Computer Club proved that this was possible using a desktop scanner and image editing programs. Nowadays, smartphones have high quality cameras and document scanning apps that do exactly the image processing needed to obtain a fingerprint from a screen, making it very easy for attackers to steal someone's identity. Thus, the approach used in this work is taking a picture of the genuine subject's screen with a document scanner app that can be downloaded for free for the phone, and let it do the common processing for documents, which is one tap away for the attacker. Then, a PAD (Presentation Attack Detection) evaluation was made to check how many attempts were successful using this technique. This evaluation was performed on 5 smartphones, using 15 capture subjects and making a total of 17,100 attack attempts. All smartphones were successfully attacked using this simple technique with at least 3 out of 4 artefact species.

Biometric systems on mobile devices are an increasingly ubiquitous method for identity verification. The majority of contemporary devices have an embedded fingerprint sensor which may be used for a variety of transactions including unlock a device or sanction a payment. In this study we explore how easy it is to successfully attack a fingerprint system using a fake finger manufactured from commonly available materials. Importantly our attackers were novices to producing the fingers and were also constrained by time. Our study shows the relative ease that modern devices can be attacked and the material combinations that lead to these attacks.

Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both.