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This practical and accessible textbook/reference describes the theory and methodology of digital forensic examinations, presenting examples developed in collaboration with police authorities to ensure relevance to real-world practice. The coverage includes discussions on forensic artifacts and constraints, as well as forensic tools used for law enforcement and in the corporate sector. Emphasis is placed on reinforcing sound forensic thinking, and gaining experience in common tasks through hands-on exercises. This enhanced third edition describes practical digital forensics with open-source tools and includes an outline of current challenges and research directions. Topics and features: Outlines what computer forensics is, and what it can do, as well as what its limitations are Discusses both the theoretical foundations and the fundamentals of forensic methodology Reviews broad principles that are applicable worldwide Explains how to find and interpret several important artifacts Describes free and open-source software tools Features content on corporate forensics, ethics, SQLite databases, triage, and memory analysis Includes new supporting video lectures on YouTube This easy-to-follow primer is an essential resource for students of computer forensics, and will also serve as a valuable reference for practitioners seeking instruction on performing forensic examinations. Joakim Kävrestad is a lecturer and researcher at Jönköping School of Engineering, Sweden, and an AccessData Certified Examiner. He also serves as a forensic consultant, with several years of experience as a forensic expert with the Swedish police. Marcus Birath is an adjunct senior lecturer in informatics at the University of Skövde. Nathan Clarke is a Professor of Cyber Security and Digital Forensics at the University of Plymouth. He is also an adjunct Professor at Edith Cowan University, Western Australia.

Data protection. --- Forensic sciences. --- Computer vision. --- Criminology. --- Data and Information Security. --- Forensic Science. --- Computer Vision. --- Crime Control and Security.

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This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addresses techniques and methodologies related to digital image forensics. It discusses advanced techniques for image manipulation detection, including the use of deep learning architectures to generate and manipulate synthetic satellite images. This book also explores methods for face recognition under adverse conditions and the importance of forensics in criminal investigations. Additionally, the book highlights anti-forensic measures applied to photos and videos, focusing on their effectiveness and trade-offs. The second area of this book focuses on the broader landscape of security, including the detection of synthetic human voices, secure deep neural networks (DNNs) and federated learning in the context of machine learning security. It investigates novel methods for detecting synthetic human voices using neural vocoder artifacts, and it explores the vulnerabilities and security challenges of federated learning in the face of adversarial attacks. Furthermore, this book delves into the realms of linguistic steganography and steganalysis, discussing the evolving techniques that utilize deep learning and natural language processing to enhance payload and detection accuracy. Overall, this book provides a comprehensive overview of the ever-evolving field of digital forensics and security, making it an invaluable resource for researchers and students interested in image forensics, machine learning security and information protection. It equips readers with the latest knowledge and tools to address the complex challenges posed by the digital landscape. Professionals working in this related field will also find this book to be a valuable resource.

Computer security. --- Digital forensic science. --- Machine learning. --- Image processing --- Computer vision. --- Computer crimes. --- Signal processing. --- Artificial intelligence. --- Machine Learning. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Computer Crime. --- Signal, Speech and Image Processing. --- Artificial Intelligence. --- Digital techniques.