Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning

In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques

Multi-privacy biometric protection scheme using ensemble systems

Biometric systems use personal biological or behavioural traits that can uniquely characterise an individual but this uniqueness property also becomes its potential weakness when the template characterising a biometric trait is stolen or compromised. To this end, we consider two strategies to improving biometric template protection and performance, namely, (1) using multiple privacy schemes and (2) using multiple matching algorithms. While multiple privacy schemes can improve the security of a biometric system by protecting its template; using multiple matching algorithms or similarly, multiple biometric traits along with their respective matching algorithms, can improve the system performance due to reduced intra-class variability. The above two strategies lead to a novel, ensemble system that is derived from multiple privacy schemes. Our findings suggest that, under the worst-case scenario evaluation where the key or keys protecting the template are stolen, multi-privacy protection scheme can outperform a single protection scheme as well as the baseline biometric system without template protection

Privacy-Preserving Biometric Authentication

Biometric-based authentication provides a highly accurate means of authentication without requiring the user to memorize or possess anything. However, there are three disadvantages to the use of biometrics in authentication; any compromise is permanent as it is impossible to revoke biometrics; there are significant privacy concerns with the loss of biometric data; and humans possess only a limited number of biometrics, which limits how many services can use or reuse the same form of authentication. As such, enhancing biometric template security is of significant research interest. One of the methodologies is called cancellable biometric template which applies an irreversible transformation on the features of the biometric sample and performs the matching in the transformed domain. Yet, this is itself susceptible to specific classes of attacks, including hill-climb, pre-image, and attacks via records multiplicity. This work has several outcomes and contributions to the knowledge of privacy-preserving biometric authentication. The first of these is a taxonomy structuring the current state-of-the-art and provisions for future research. The next of these is a multi-filter framework for developing a robust and secure cancellable biometric template, designed specifically for fingerprint biometrics. This framework is comprised of two modules, each of which is a separate cancellable fingerprint template that has its own matching and measures. The matching for this is based on multiple thresholds. Importantly, these methods show strong resistance to the above-mentioned attacks. Another of these outcomes is a method that achieves a stable performance and can be used to be embedded into a Zero-Knowledge-Proof protocol. In this novel method, a new strategy was proposed to improve the recognition error rates which is privacy-preserving in the untrusted environment. The results show promising performance when evaluated on current datasets

Cancellable face template algorithm based on speeded-up robust features and winner-takes-all

Features such as face, fingerprint, and iris imprints have been used for authentication in biometric system. The toughest feature amongst these is the face. Extracting a region with the most potential face features from an image for biometric identification followed by illumination enhancement is a commonly used method. However, the region of interest extraction followed by illumination enhancement is sensitive to image face feature displacement, skewed image, and bad illumination. This research presents a cancell able face image algorithm built upon the speeded-up robust features method to extract and select features. A speeded-up robust feature approach is utilised for the image’s features extraction, while Winner-Takes-All hashing is utilised for match-seeking. Finally, the features vectors are projected by utilising a random form of binary orthogonal matrice. Experiments were conducted on Yale and ORL datasets which provide gray scale images of sizes 168 × 192 and 112 × 92 pixels, respectively. The execution of the proposed algorithm was measured against several algorithms using equal error rate metric. It is found that the proposed algorithm produced an acceptable performance which indicates that this algorithm can be used in biometric security applications

Multimedia security and privacy protection in the internet of things: research developments and challenges

With the rapid growth of the internet of things (IoT), huge amounts of multimedia data are being generated from and/or exchanged through various IoT devices, systems and applications. The security and privacy of multimedia data have, however, emerged as key challenges that have the potential to impact the successful deployment of IoT devices in some data-sensitive applications. In this paper, we conduct a comprehensive survey on multimedia data security and privacy protection in the IoT. First, we classify multimedia data into different types and security levels according to application areas. Then, we analyse and discuss the existing multimedia data protection schemes in the IoT, including traditional techniques (e.g., cryptography and watermarking) and emerging technologies (e.g., blockchain and federated learning). Based on the detailed analysis on the research development of IoT-related multimedia security and privacy protection, we point out some open challenges and provide future research directions, aiming to advance the study in the relevant fields and assist researchers in gaining a deeper understanding of the state of the art on multimedia data protection in the IoT

Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution

A master face is a face image that passes face-based identity authentication for a high percentage of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information. We optimize these faces for 2D and 3D face verification models, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. For 2D face verification, multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network to direct the search toward promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a considerable coverage of the identities in the LFW or RFW datasets with less than 10 master faces, for six leading deep face recognition systems. In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network. When employing two different 3D face recognition systems, we are able to obtain a coverage of 40%-50%. Additionally, we present the generation of paired 2D RGB and 3D master faces, which simultaneously match 2D and 3D models with high impersonation rates.Comment: accepted for publication in IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM). This paper extends arXiv:2108.01077 that was accepted to IEEE FG 202

A Survey on Biometrics and Cancelable Biometrics Systems

Now-a-days, biometric systems have replaced the password or token based authentication system in many fields to improve the security level. However, biometric system is also vulnerable to security threats. Unlike password based system, biometric templates cannot be replaced if lost or compromised. To deal with the issue of the compromised biometric template, template protection schemes evolved to make it possible to replace the biometric template. Cancelable biometric is such a template protection scheme that replaces a biometric template when the stored template is stolen or lost. It is a feature domain transformation where a distorted version of a biometric template is generated and matched in the transformed domain. This paper presents a review on the state-of-the-art and analysis of different existing methods of biometric based authentication system and cancelable biometric systems along with an elaborate focus on cancelable biometrics in order to show its advantages over the standard biometric systems through some generalized standards and guidelines acquired from the literature. We also proposed a highly secure method for cancelable biometrics using a non-invertible function based on Discrete Cosine Transformation (DCT) and Huffman encoding. We tested and evaluated the proposed novel method for 50 users and achieved good results