Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initialization of the PAD model is a crucial factor for the generalization, which is rarely discussed in the community. Based on such observation, we proposed a self-supervised learning-based method, denoted as DF-DM. Specifically, DF-DM is based on a global-local view coupled with De-Folding and De-Mixing to derive the task-specific representation for PAD. During De-Folding, the proposed technique will learn region-specific features to represent samples in a local pattern by explicitly minimizing generative loss. While De-Mixing drives detectors to obtain the instance-specific features with global information for more comprehensive representation by minimizing interpolation-based consistency. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both face and fingerprint PAD in more complicated and hybrid datasets when compared with state-of-the-art methods. When training in CASIA-FASD and Idiap Replay-Attack, the proposed method can achieve an 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding baseline performance by 9.54%. The source code of the proposed technique is available at https://github.com/kongzhecn/dfdm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Feature Fusion for Fingerprint Liveness Detection

For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s ridge-valley patterns. Due to the criticality of this threat, it is crucial to develop countermeasure methods capable of facing and preventing these kind of attacks. The most effective counter–spoofing methods are those trying to distinguish between a "live" and a "fake" fingerprint before it is actually submitted to the recognition system. According to the technology used, these methods are mainly divided into hardware and software-based systems. Hardware-based methods rely on extra sensors to gain more pieces of information regarding the vitality of the fingerprint owner. On the contrary, software-based methods merely rely on analyzing the fingerprint images acquired by the scanner. Software-based methods can then be further divided into dynamic, aimed at analyzing sequences of images to capture those vital signs typical of a real fingerprint, and static, which process a single fingerprint impression. Among these different approaches, static software-based methods come with three main benefits. First, they are cheaper, since they do not require the deployment of any additional sensor to perform liveness detection. Second, they are faster since the information they require is extracted from the same input image acquired for the identification task. Third, they are potentially capable of tackling novel forms of attack through an update of the software. The interest in this type of counter–spoofing methods is at the basis of this dissertation, which addresses the fingerprint liveness detection under a peculiar perspective, which stems from the following consideration. Generally speaking, this problem has been tackled in the literature with many different approaches. Most of them are based on first identifying the most suitable image features for the problem in analysis and, then, into developing some classification system based on them. In particular, most of the published methods rely on a single type of feature to perform this task. Each of this individual features can be more or less discriminative and often highlights some peculiar characteristics of the data in analysis, often complementary with that of other feature. Thus, one possible idea to improve the classification accuracy is to find effective ways to combine them, in order to mutually exploit their individual strengths and soften, at the same time, their weakness. However, such a "multi-view" approach has been relatively overlooked in the literature. Based on the latter observation, the first part of this work attempts to investigate proper feature fusion methods capable of improving the generalization and robustness of fingerprint liveness detection systems and enhance their classification strength. Then, in the second part, it approaches the feature fusion method in a different way, that is by first dividing the fingerprint image into smaller parts, then extracting an evidence about the liveness of each of these patches and, finally, combining all these pieces of information in order to take the final classification decision. The different approaches have been thoroughly analyzed and assessed by comparing their results (on a large number of datasets and using the same experimental protocol) with that of other works in the literature. The experimental results discussed in this dissertation show that the proposed approaches are capable of obtaining state–of–the–art results, thus demonstrating their effectiveness

Integration of statistical method and zernike moment as feature extraction in liveness detection

Recently, fake fingerprints have been used to defeat fingerprint recognition systems. These fake fingerprints are created without the need for any expertise and use easily found materials. In this paper, a fake fingerprint detection method is proposed that employs a combination of eleven statistical methods and integrating them with Zernike Moment as the feature extractor. Based on the experimental results, the proposed method showed average classification accuracy, sensitivity and specificity of approximately 80% for all sensors used to capture fake fingerprint images fabricated by gelatine and latex materials

An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification

Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is denoted as SSL-ART. Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy ARTMAP structure to map the established prototype nodes to the target classes using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is devised to associate a prototype node with more than one class label. The main advantages of SSL-ART include the capability of: (i) performing online learning, (ii) reducing the number of redundant prototype nodes through the OtM mapping scheme and minimizing the effects of noisy samples, and (iii) providing an explanation facility for users to interpret the predicted outcomes. In addition, a weighted voting strategy is introduced to form an ensemble SSL-ART model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART, assigns {\color{black}a different weight} to each class based on its performance pertaining to the corresponding class. The aim is to mitigate the effects of training data sequences on all SSL-ART members and improve the overall performance of WESSL-ART. The experimental results on eighteen benchmark data sets, three artificially generated data sets, and a real-world case study indicate the benefits of the proposed SSL-ART and WESSL-ART models for tackling pattern classification problems.Comment: 13 pages, 8 figure

Transfer Learning of Pre-Trained CNN Models for Fingerprint Liveness Detection

Machine learning experts expected that transfer learning will be the next research frontier. Indeed, in the era of deep learning and big data, there are many powerful pre-trained CNN models that have been deployed. Therefore, using the concept of transfer learning, these pre-trained CNN models could be re-trained to tackle a new pattern recognition problem. As such, this work is aiming to investigate the application of transferred VGG19-based CNN model to solve the problem of fingerprint liveness recognition. In particular, the transferred VGG19-based CNN model will be modified, re-trained, and finely tuned to recognize real and fake fingerprint images. Moreover, different architecture of the transferred VGG19-based CNN model has examined including shallow model, medium model, and deep model. To assess the performances of each architecture, LivDet2009 database was employed. Reported results indicated that the best recognition rate was achieved from shallow VGG19-based CNN model with 92% accuracy

Detecting CAN Attacks on J1939 and NMEA 2000 Networks

J1939 is a networking layer built on top of the widespread CAN bus used for communication between different subsystems within a vehicle. The J1939 and NMEA 2000 protocols standardize data enrichment for these subsystems, and are used for trucks, weapon systems, naval vessels, and other industrial systems. Practical security solutions for existing CAN based communication systems are notoriously difficult because of the lack of cryptographic capabilities of the devices involved. In this paper we propose a novel intrusion detection system (IDS) for J1939 and NMEA 2000 networks. Our IDS (CANDID) combines timing analysis with a packet manipulation detection system and data analysis. This data analysis enables us to capture the state of the vehicle, detect messages with irregular timing intervals, and take advantage of the dependencies between different Electronic Control Units (ECUs) to restrict even the most advanced attacker. Our IDS is deployed and tested on multiple vehicles, and has demonstrated greater accuracy and detection capabilities than previous work