Fingerphoto spoofing in mobile devices: A preliminary study (original) (raw)
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Spoofing Protection for Biometric Systems
International Journal of Science Technology & Engineering
A biometric system is a computer system used to identify people based on their behavioral and physiological characteristics. In such kinds of systems, the security is still a question mark because of various types of intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. This paper introduces a novel software-based fake detection method that can be used in multiple biometric systems (fingerprint, iris, face, palm print) to detect different types of fraudulent access attempts through the use of image quality assessment. The proposed approach uses 25 general image quality features extracted from a single image to distinguish between real and fake samples. The system proves to be efficient for the protection against different spoofing attacks.
IRJET-Secure Face Spoof Detection on Smartphone
Now a day, smart phone users are increasing rapidly. Information access from Smart-phones and tablets has become main stream both in business and personal environments over the last years. The use of these devices for accessing services like social networks, email or electronic commerce and banking has surpassed the access from traditional computers, turning mobile devices into essential tools in our everyday life. People use simple passwords, they reuse them on different accounts and services, passwords can be shared and cracked, etc. As a result, biometric technologies are now offered as alternatives to passwords, including face authentication on devices with front-facing cameras. However, face authentication is vulnerable to spoofing attacks, to address these issues with face authentication, we take the first step to design Secure Face Detection System On Smart-phone for device unlock. Meanwhile, most of existing databases only concentrate on the antispoofing of different kinds of attacks and ignore the environmental changes in real world applications. In proposed system, we focus on public-domain face spoof databases will show that the proposed approach is effective in face spoof detection for both cross-database and intra-database testing scenarios.
Fake finger detection by skin distortion analysis
IEEE Transactions on Information Forensics and Security, 2006
Attacking fingerprint-based biometric systems by presenting fake fingers at the sensor could be a serious threat for unattended applications. This work introduces a new approach for discriminating fake fingers from real ones, based on the analysis of skin distortion. The user is required to move the finger while pressing it against the scanner surface, thus deliberately exaggerating the skin distortion. Novel techniques for extracting, encoding and comparing skin distortion information are formally defined and systematically evaluated over a test set of real and fake fingers. The proposed approach is privacy friendly and does not require additional expensive hardware besides a fingerprint scanner capable of capturing and delivering frames at proper rate. The experimental results indicate the new approach to be a very promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing attempts.
Personal Authentication Using Finger Images
Citation/Export MLA Renuka Mahesh Jadhav, Prof. Dr. D. S. Bormane, “Personal Authentication Using Finger Images”, January 15 Volume 3 Issue 1 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 389 - 393, DOI: 10.17762/ijritcc2321-8169.150177 APA Renuka Mahesh Jadhav, Prof. Dr. D. S. Bormane, January 15 Volume 3 Issue 1, “Personal Authentication Using Finger Images”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 389 - 393, DOI: 10.17762/ijritcc2321-8169.150177
Evaluation of biometric spoofing in a multimodal system
2010
Multimodal biometric systems have consistently presented better recognition rates when compared to the unimodal systems that compose them. A common claim is that they also provide higher security when compared to unimodal systems since an intruder would have to successfully break into more than one biometrical system. We argue that this may not be true due to two main reasons: a multimodal system has a higher number of vulnerable points that may be explored by an intruder, and an intruder may break the multimodal system by attacking only a subset of the unimodal systems. In particular, we investigate a multimodal system composed of face and fingerprint under different spoof attack scenarios. In this case, a forger may choose to spoof the face or fingerprint traits. We empirically show that the false acceptance rate increases dramatically when either mode is spoofed, which means that an intruder may be falsely authenticated by spoofing only one mode.
Smartphone Multi-modal Biometric Authentication: Database and Evaluation
arXiv (Cornell University), 2019
Biometric-based verification is widely employed on the smartphones for various applications, including financial transactions. In this work, we present a new multimodal biometric dataset (face, voice, and periocular) acquired using a smartphone. The new dataset is comprised of 150 subjects that are captured in six different sessions reflecting real-life scenarios of smartphone assisted authentication. One of the unique features of this dataset is that it is collected in four different geographic locations representing a diverse population and ethnicity. Additionally, we also present a multimodal Presentation Attack (PA) or spoofing dataset using a low-cost Presentation Attack Instrument (PAI) such as print and electronic display attacks. The novel acquisition protocols and the diversity of the data subjects collected from different geographic locations will allow developing a novel algorithm for either unimodal or multimodal biometrics.Further, we also report the performance evaluation of the baseline biometric verification and Presentation Attack Detection (PAD) on the newly collected dataset.
Fingerprints Image Spoof Detection and Classification
Biometric identification is gaining more and more recognition as a leading technology for identity management and security systems. This inevitably is followed by numerous methods that are aimed at breaking those security measures. In this paper we address the problem of fingerprints spoofing, that is, the usage of counterfeit elastic fingerprints. Based on image features extracted from 2D fingerprints images, we present several novel algorithms that classify fingerprints images as real or fake, and demonstrate their performance on a fingerprints database. We conclude that it is possible to achieve a relatively high level of confidence using our approach.
Securing Online Transaction using Fingerprint Authentication with Embedded Cameras
Now a day's mobile phone became smart phone with lot of features. Smart phone comes with high resolution cameras and support high speed internet. This tends to increase the use of online transactions. But these is secured only by ID no. & password, this is not so secured. Biometric characteristics like fingerprint are changes person to person. So to increase the security of online transactions we use Fingerprint recognition with credit card/debit card transaction. Smart phone with high pixel camera function are capable of capturing image & processing task. In this proposed system cell phones cameras capturing fingerprint images as biometric traits. No need of extra module for fingerprint recognition. Everyday a lot of new mobile phones called as smart phones come in a market with various features like embedded cameras, Fast processors, pocket high speed Internet & many more. By using embedded camera we can take photos & shoot videos. Some of embedded cameras have high resolution & high picture quality images more than 5 Mega-Pixels. Due to high speed internet almost all banking technology has changed to online. So the traditional way of shopping is changed to Internet shopping also we can pay the various bills, transfer the money by using online transactions. But security of online transactions is a big issue. Now days this system is secured only by credit card/debit card no/ ID no, CVC no. & OTP (one time password) which is send on registered mobile no. Moreover, the services which can be accessed via smart phones (e.g., m-banking and m-commerce etc.) represent a major value. Therefore, the danger of a mobile device ending up in the wrong hands presents a serious threat to information security and user privacy. According to the latest research from Halifax Home Insurance claims, 390 million British pounds a year is lost in Britain due to the theft of smart phones. With the average handset costing more than 100 British pounds, it is perhaps not surprising that there are more than 2 million stolen in the UK [1] & India every year. Biometric characteristics like fingerprint, voice pattern, iris etc cannot be stolen or forgotten & also biometric characteristics are unique & remain same even fingerprints of twins are different. So it's most promising technology for authentication. Approximately from 14 th century fingerprints were stamping on paper using ink for identification of person. Now days they are captured as live-scan digital images acquired by directly sensing the fingerprint surface with an electronic fingerprint scanner. The fingerprint pattern displays different features at different levels. Some smart phone has inbuilt fingerprint scanner. But they are very costly. Many fingerprint recognition algorithms perform well on databases that had been collected with high-resolution cameras and in highly controlled situations [2]. In this paper we present fingerprint recognition as means of verifying the identity of the user using embedded camera. We use Fingerprint of user as a password for online transactions. The image of fingerprint is captured by using embedded camera of smart phone. Mostly more than 5 Mega-pixel cameras are used for capturing the image of fingerprint traits. This image is compared with the database. If the image is matched with the database then user can do the online transactions. This is the most secure and easy method. The main purpose of this paper is to lower down the user effort while keeping the error rates in an acceptable and practical range. Therefore, this proposal is a realistic approach to be implemented in mobile devices for user authentication. II Fingerprint Recognition Fingerprint recognition is the most matured approach among all the biometric techniques. With its success of use in different applications, it is today used in many access controls applications as each individual has a unique fingerprint. The hand skin or the finger skin consists of the so called friction ridges with pores. The ridges are already created in the ninth week of an individual's fetal development life [3], and remains the same all life long, only growing up to adult size, but if severe injuries occur the skin may be reconstructed the same as before. Researchers have found out that identical twins have fingerprints that are quite different and that in the forensic community it is believed that no two people have the same fingerprint [4].
Spoofing Detectability as a Property of Biometric Characteristics
2021
Regardless of the application domain, adversaries may conduct spoofing attacks in order to bypass an authentication system. The difficulty of fooling a biometric sensor, known as circumvention; can be paired with an additional property based on the easiness of identifying ongoing presentation attacks which could help selecting the most suitable characteristic(s) when designing a biometric system. To such extent, this paper proposes spoofing detectability, as a property of biometric characteristics, to indicate the likelihood of detecting ongoing presentation attacks aiming at overcoming authentication mechanisms. We define and then quantitatively estimate spoofing detectability through unsupervised anomaly detection on publicly available biometric datasets, collecting metric scores which are then converted into the Low, Medium, High categories for 8 different biometric characteristics. We built our results upon unsupervised algorithms as they represent the most suitable answer to the detection of zero-day attacks. Alongside with our experimental process, we show the intrinsic relevance of spoofing detectability to complement circumvention. As a final contribution of the paper, we show how to embed an anomaly-based spoofing detection module into an authentication system for runtime support.
To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.