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Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model’s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning. This book covers the motivations, fundamentals, techniques and protocols for protecting ML models using watermarking. Furthermore, it showcases cutting-edge work in e.g. model watermarking, signature and passport embedding and their use cases in distributed federated learning settings.
This book is a collection of best selected research papers presented at International Conference on Intelligent and Smart Computing in Data Analytics (ISCDA 2020), held at K L University, Guntur, Andhra Pradesh, India. The primary focus is to address issues and developments in advanced computing, intelligent models and applications, smart technologies and applications. It includes topics such as artificial intelligence and machine learning, pattern recognition and analysis, computational intelligence, signal and image processing, bioinformatics, ubiquitous computing, genetic fuzzy systems, hybrid evolutionary algorithms, nature-inspired smart hybrid systems, Internet of things, industrial IoT, health informatics, human–computer interaction and social network analysis. The book presents innovative work by leading academics, researchers and experts from industry.
Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession. - This new edition now contains essential information on steganalysis and steganography - New concepts and new applications including QIM introduced - Digital watermark embedding is given a complete update with new processes and applications
This book presents medical image watermarking techniques and algorithms for telemedicine and other emerging applications. This book emphasizes on medical image watermarking to ensure the authenticity of transmitted medical information. It begins with an introduction of digital watermarking, important characteristics, novel applications, different watermarking attacks and standard benchmark tools. This book also covers spatial and transform domain medical image watermarking techniques and their merits and limitations. The authors have developed improved/novel watermarking techniques for telemedicine applications that offer higher robustness, better perceptual quality and increased embedding capacity and secure watermark. The suggested methods may find potential applications in the prevention of patient identity theft and health data management issues which is a growing concern in telemedicine applications. This book provides a sound platform for understanding the medical image watermarking paradigm for researchers in the field and advanced-level students. Industry professionals working in this field, as well as other emerging applications demanding robust and secure watermarking will find this book useful as a reference.
A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors – noted experts on the topic – offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal Data Processing brings together in one volume the range of multi-modal data processing.
This book comprises the proceedings of the International Conference on Machine Vision and Augmented Intelligence (MAI 2021) held at IIIT, Jabalpur, in February 2021. The conference proceedings encapsulate the best deliberations held during the conference. The diversity of participants in the event from academia, industry, and research reflects in the articles appearing in the volume. The book theme encompasses all industrial and non-industrial applications in which a combination of hardware and software provides operational guidance to devices in the execution of their functions based on the capture and processing of images. This book covers a wide range of topics such as modeling of disease transformation, epidemic forecast, COVID-19, image processing and computer vision, augmented intelligence, soft computing, deep learning, image reconstruction, artificial intelligence in healthcare, brain-computer interface, cybersecurity, and social network analysis, natural language processing, etc.
Multimedia security has become a major research topic, yielding numerous academic papers in addition to many watermarking-related companies. In this emerging area, there are many challenging research issues that deserve sustained study towards an effective and practical system. This book explores the myriad of issues regarding multimedia security, including perceptual fidelity analysis, image, audio, and 3D mesh object watermarking, medical watermarking, error detection (authentication) and concealment, fingerprinting, digital signature and digital right management.
This volume constitutes the proceedings of the 20th International Workshop on Digital Forensics and Watermarking, IWDW 2021, held in Beijing, China, in November 2021. The 18 full papers in this volume were carefully reviewed and selected from 32 submissions. The are categorized in the following topical headings: Forensics and Security Analysis; Watermarking and Steganology.
Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This book is an introduction to steganalysis as part of the wider trend of multimedia forensics, as well as a practical tutorial on machine learning in this context. It looks at a wide range of feature vectors proposed for steganalysis with performance tests and comparisons. Python programs and algorithms are provided to allow readers to modify and reproduce outcomes discussed in the book.
Lossless Information Hiding in Images introduces many state-of-the-art lossless hiding schemes, most of which come from the authors' publications in the past five years. After reading this book, readers will be able to immediately grasp the status, the typical algorithms, and the trend of the field of lossless information hiding. Lossless information hiding is a technique that enables images to be authenticated and then restored to their original forms by removing the watermark and replacing overridden images. This book focuses on the lossless information hiding in our most popular media, images, classifying them in three categories, i.e., spatial domain based, transform domain based, and compressed domain based. Furthermore, the compressed domain based methods are classified into VQ based, BTC based, and JPEG/JPEG2000 based. - Focuses specifically on lossless information hiding for images - Covers the most common visual medium, images, and the most common compression schemes, JPEG and JPEG 2000 - Includes recent state-of-the-art techniques in the field of lossless image watermarking - Presents many lossless hiding schemes, most of which come from the authors' publications in the past five years