Detection of fingerprint alterations using deep convolutional neural networks

Fingerprint alteration is a challenge that poses enormous security risks. As a result, many research efforts in the scientific community have attempted to address this issue. However, non-existence of publicly available datasets that contain obfuscation and distortion of fingerprints makes it difficult to identify the type of alteration. In this work we present the publicly available Sokoto-Coventry Fingerprints Dataset (SOCOFing), which provides ten fingerprints for 600 different subjects, as well as gender, hand and finger name for each image, among other unique characteristics. We also provide a total of 55,249 images with three levels of alteration for Z-cut, obliteration and central rotation synthetic alterations, which are the most common types of obfuscation and distortion. In addition, this paper proposes a Convolutional Neural Network (CNN) to identify these alterations. The proposed CNN model achieves a classification accuracy rate of 98.55%. Results are also compared with a residual CNN model pre-trained on ImageNet, which produces an accuracy of 99.88%

Blind Image Quality Assessment for Face Pose Problem

No-Reference image quality assessment for face images is of high interest since it can be required for biometric systems such as biometric passport applications to increase system performance. This can be achieved by controlling the quality of biometric sample images during enrollment. This paper proposes a novel no-reference image quality assessment method that extracts several image features and uses data mining techniques for detecting the pose variation problem in facial images. Using subsets from three public 2D face databases PUT, ENSIB, and AR, the experimental results recorded a promising accuracy of 97.06% when using the RandomForest Classifier, which outperforms other classifier

Quality-dependent fusion system using no-reference image quality metrics for multimodal biometrics

Biometric acquired and processed data quality is the prime influences which will affect the performance of the whole biometric system. Hence, aforementioned is essential to control the quality of acquired data to devise a suitable biometric system. This paper presents a robust multimodal biometric system using quality dependent expert fusion system. We Presents work, on a novel quality assessment metrics for Fingerprint, Palmprint, and Iris. The originality of this work contributing with blind image quality measures. The projected quality metrics associates with two type of quality measure a) Image-based quality as well as b) pattern-based. We have explore and comprehend the associated various quality assessment in the biometrics. Benefits of the proposed quality matric have been illustrates on six benchmark database. The performance of the proposed quality measures demonstrates on multimodal biometric system is evaluated on a public dataset and demonstrating its recognition accuracy with respect to EER. Result shows the efficiency of detecting the kind of alterations. Kolmogorov-Smirnov (KS) test statistics shows 0.84 to 0.94 outperformed as compared to NFIQ

Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy

Fingerprint Classification Using Transfer Learning Technique

Fingerprints play a significant role in many sectors. Nowadays, fingerprints are used for identification purposes in criminal investigations. They are also used as an authentication method since they are considered more secure than passwords. Fingerprint sensors are already widely deployed in many devices, including mobile phones and smart locks. Criminals try to compromise biometric fingerprint systems by purposely altering their fingerprints or entering fake ones. Therefore, it is critical to design and develop a highly accurate fingerprint classification. However, some fingerprint datasets are small and not sufficient to train a neural network. Thus, transfer learning is utilized. A large Sokoto Coventry Fingerprint Dataset (SOCOFing), which contains 55,273 fingerprint images, was first used to train a convolutional neural network model to detect image alteration and level of alternations. The model was able to achieve an 81% of accuracy. Then, a few layers of SOCOFing model were used and adapted to train another smaller dataset, namely ATVS-FakeFingerprint Database (ATVS-FFp DB), which contains 3,168 fingerprint images. Two models were trained. The first transferring model was built to classify images into real and fake, and a remarkable classification accuracy of 99.4% was achieved. The second transferring model was used to detect if the image was fake and if the user was cooperating in the generated faked fingerprint. The model achieved a classification accuracy of 97.5%. The transfer learning technique proves to be very effective in addressing insufficient dataset issues for deep learning

Face Liveness Detection under Processed Image Attacks

Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques

Deep fingerprint classification network

Fingerprint is one of the most well-known biometrics that has been used for personal recognition. However, faked fingerprints have become the major enemy where they threat the security of this biometric. This paper proposes an efficient deep fingerprint classification network (DFCN) model to achieve accurate performances of classifying between real and fake fingerprints. This model has extensively evaluated or examined parameters. Total of 512 images from the ATVS-FFp_DB dataset are employed. The proposed DFCN achieved high classification performance of 99.22%, where fingerprint images are successfully classified into their two categories. Moreover, comparisons with state-of-art approaches are provided

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Detecting Double-Identity Fingerprint Attacks

Double-identity biometrics, that is the combination of two subjects features into a single template, was demonstrated to be a serious threat against existing biometric systems. In fact, well-synthetized samples can fool state-of-the-art biometric verification systems, leading them to falsely accept both the contributing subjects. This work proposes one of the first techniques to defy existing double-identity fingerprint attacks. The proposed approach inspects the regions where the two aligned fingerprints overlap but minutiae cannot be consistently paired. If the quality of these regions is good enough to minimize the risk of false or miss minutiae detection, then the alarm score is increased. Experimental results carried out on two fingerprint databases, with two different techniques to generate double-identity fingerprints, validate the effectiveness of the proposed approach