As information technology is rapidly progressing, an enormous amount of media can be easily exchanged through Internet and other communication networks. Increasing amounts of digital image, video, and music have created numerous information security issues and is now taken as one of the top research and development agendas for researchers, organizations, and governments worldwide. ""Multimedia Forensics and Security"" provides an in-depth treatment of advancements in the emerging field of multimedia forensics and security by tackling challenging issues such as digital watermarking for copyright protection, digital fingerprinting for transaction tracking, and digital camera source identification.

The tools of crime constantly evolve, and law enforcement and forensic investigators must understand advanced forensic techniques to ensure that the most complete evidence is brought to trial. Paramount also the need for investigators to ensure that evidence adheres to the boundaries of the legal system, a place where policy often lags behind new innovations. Crime Prevention Technologies and Applications for Advancing Criminal Investigation addresses the use of electronic devices and software for crime prevention, investigation, and the application of a broad spectrum of sciences to answer questions of interest to the legal system. This book fosters a forum for advancing research and development of the theory and practice of digital crime prevention and forensics.

Due to the rise of digital crime and the pressing need for methods of combating these forms of criminal activities, there is an increasing awareness of the importance of digital forensics and investigation. The Handbook of Research on Computational Forensics, Digital Crime, and Investigation: Methods and Solutions addresses a broad range of electronic devices and software for crime prevention and investigation. This defining body of research covers a wide spectrum of topics useful to a broad cross-sectional and multi-disciplinary readership ranging from academic and professional research communities to industry consultants and practitioners.

Central to understanding and combating digital crime is the ability to develop new methods for the collection and analysis of electronic evidence. New Technologies for Digital Crime and Forensics: Devices, Applications, and Software provides theories, methods, and studies on digital crime prevention and investigation, which are useful to a broad range of researchers and communities. This field is under constant evolution as the nature of digital crime continues to change and new methods for tracking and preventing digital attacks are developed.

The revolutionary way in which modern technologies have enabled us to exchange information with ease has created a demand for interdisciplinary research in digital forensics and investigations aiming to combat the abuse of computer technologies. Emerging Digital Forensics Applications for Crime Detection, Prevention, and Security presents various digital crime and forensic disciplines that use electronic devices and software for crime prevention and detection. This book provides theoretical and empirical research articles and case studies for a broad range of academic readers as well as professionals, industry consultants, and practitioners involved in the use, design, and development of techniques related to digital forensics and investigation.

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it "Deep Biometrics". The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications. Highlights the impact of deep learning over the field of biometrics in a wide area; Exploits the deeper and wider background of biometrics, such as privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.; Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.

This book highlights recent advances in smart cities technologies, with a focus on new technologies such as biometrics, blockchains, data encryption, data mining, machine learning, deep learning, cloud security, and mobile security. During the past five years, digital cities have been emerging as a technology reality that will come to dominate the usual life of people, in either developed or developing countries. Particularly, with big data issues from smart cities, privacy and security have been a widely concerned matter due to its relevance and sensitivity extensively present in cybersecurity, healthcare, medical service, e-commercial, e-governance, mobile banking, e-finance, digital twins, and so on. These new topics rises up with the era of smart cities and mostly associate with public sectors, which are vital to the modern life of people. This volume summarizes the recent advances in addressing the challenges on big data privacy and security in smart cities and points out the future research direction around this new challenging topic.

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it "Deep Biometrics". The book aims to highlight recent developments in biometrics using semi-supervised and unsupervised methods such as Deep Neural Networks, Deep Stacked Autoencoder, Convolutional Neural Networks, Generative Adversary Networks, and so on. The contributors demonstrate the power of deep learning techniques in the emerging new areas such as privacy and security issues, cancellable biometrics, soft biometrics, smart cities, big biometric data, biometric banking, medical biometrics, healthcare biometrics, and biometric genetics, etc. The goal of this volume is to summarize the recent advances in using Deep Learning in the area of biometric security and privacy toward deeper and wider applications. Highlights the impact of deep learning over the field of biometrics in a wide area; Exploits the deeper and wider background of biometrics, such as privacy versus security, biometric big data, biometric genetics, and biometric diagnosis, etc.; Introduces new biometric applications such as biometric banking, internet of things, cloud computing, and medical biometrics.

This book constitutes the refereed proceedings of the 16th Australasian Conference on Data Mining, AusDM 2018, held in Bathurst, NSW, Australia, in November 2018.The 27 revised full papers presented together with 3 short papers were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on classification task; transport, environment, and energy; applied data mining; privacy and clustering; statistics in data science; health, software and smartphone; image data mining; industry showcase.