Watermarking security: theory and practice

This article proposes a theory of watermarking security based on a cryptanalysis point of view. The main idea is that information about the secret key leaks from the observations, for instance watermarked pieces of content, available to the opponent. Tools from information theory (Shannon's mutual information and Fisher's information matrix) can measure this leakage of information. The security level is then defined as the number of observations the attacker needs to successfully estimate the secret key. This theory is applied to two common watermarking methods: the substitutive scheme and the spread spectrum based techniques. Their security levels are calculated against three kinds of attack. The experimental work illustrates how Blind Source Separation (especially Independent Component Analysis) algorithms help the opponent exploiting this information leakage to disclose the secret carriers in the spread spectrum case. Simulations assess the security levels derived in the theoretical part of the article

Establishing the digital chain of evidence in biometric systems

Traditionally, a chain of evidence or chain of custody refers to the chronological documentation, or paper trail, showing the seizure, custody, control, transfer, analysis, and disposition of evidence, physical or electronic. Whether in the criminal justice system, military applications, or natural disasters, ensuring the accuracy and integrity of such chains is of paramount importance. Intentional or unintentional alteration, tampering, or fabrication of digital evidence can lead to undesirable effects. We find despite the consequences at stake, historically, no unique protocol or standardized procedure exists for establishing such chains. Current practices rely on traditional paper trails and handwritten signatures as the foundation of chains of evidence.;Copying, fabricating or deleting electronic data is easier than ever and establishing equivalent digital chains of evidence has become both necessary and desirable. We propose to consider a chain of digital evidence as a multi-component validation problem. It ensures the security of access control, confidentiality, integrity, and non-repudiation of origin. Our framework, includes techniques from cryptography, keystroke analysis, digital watermarking, and hardware source identification. The work offers contributions to many of the fields used in the formation of the framework. Related to biometric watermarking, we provide a means for watermarking iris images without significantly impacting biometric performance. Specific to hardware fingerprinting, we establish the ability to verify the source of an image captured by biometric sensing devices such as fingerprint sensors and iris cameras. Related to keystroke dynamics, we establish that user stimulus familiarity is a driver of classification performance. Finally, example applications of the framework are demonstrated with data collected in crime scene investigations, people screening activities at port of entries, naval maritime interdiction operations, and mass fatality incident disaster responses

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Proactive defense strategies against net load redistribution attacks in cyber-physical smart grids

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringHongyu WuRecent advances in the cyber-physical smart grid (CPSG) have enabled a broad range of new devices based on information and communication technology (ICT). An open network environment in CPSG provides frequent interaction between information and physical components. However, this interaction also exposes the ICT-enabled devices to a growing threat of cyberattacks. Such threats have been alerted by recent cybersecurity incidents, and the security issues have strongly restricted the development of CPSG. Among various CPS cybersecurity incidents, cyber data attacks invade the cyber layer to destroy data integrity. Through elaborately eavesdropping on the transferred measurement data, the attacks can mislead the state estimation (SE) while keeping stealthy to conventional bad data detection (BDD). Due to the SE being the critical function of CPSG control, the cyber data attacks may cause massive economic loss, power system instability, or even cascading failures. Therefore, this dissertation focuses on the detection of stealthy data integrity attacks. This dissertation first performs a thorough review of the state-of-the-art cyber-physical security of the smart grid. By focusing on the physical layer of the CPSG, this work provides an abstracted and unified state-space model in which cyber-physical attack and defense models can be effectively generalized. The existing cyber-physical attacks are categorized in terms of their target components. In addition, this work discusses several operational and informational defense approaches that present the current state-of-the-art in the field, including moving target defense (MTD), watermarking, and data-driven strategies. The challenges and future opportunities associated with the smart grid cyber-physical security is also discussed. Further, a real-time digital simulator, namely Typhoon HIL, is utilized to visualize the random MTD against false data injection (FDI) attacks. Given the review section as a background, a hidden, coordinated net load redistribution attack (NLRA) in an AC distribution system is proposed. The attacker's goal is to create violations in nodal voltage magnitude estimation. An attacker can implement the NLRA strategy by using the local information of an attack region and power flow enhanced deep learning (PFEDL) state estimators. The NLRA is modeled as an attacker's modified AC optimal power flow problem to maximize the attack impact. Case study results indicate the PFEDL-based SE can provide the attacker with accurate system states in a low observable distribution system where conventional lease square-based SE cannot converge. The stealthiness of the hidden NLRA is validated in multiple attack cases. The influence of NLRA on the distribution system is assessed, and the impact of attack regions, attack timing, and attack area size are also revealed. Next, this dissertation highlights that current MTD strategies myopically perturb the reactance of D-FACTS lines without considering the system voltage stability. Voltage instability induced by MTDs is illustrated in a three-bus system and two more complicated systems with real-world load profiles. Further, a novel MTD framework that explicitly considers system voltage stability using continuation power flow and voltage stability indices is proposed to avoid MTD-induced voltage instability. In addition, this dissertation mathematically derives the sensitivity matrix of voltage stability index to line impedance, on which an optimization problem for maximizing voltage stability index is formulated. This framework is tested on the IEEE 14-bus and the IEEE 118-bus transmission systems, in which sophisticated attackers launch NLRAs. The simulation results show the effectiveness of the proposed framework in circumventing voltage instability while maintaining the detection effectiveness of MTD. Case studies are conducted with and without the proposed framework under different MTD planning and operational methods. The impacts of the proposed two methods on attack detection effectiveness and system economic metrics are also revealed. Finally, this dissertation proposes utilizing smart inverters to implement a novel meter encoding scheme in distribution systems. The proposed meter encoding scheme is a software-based active detection method, which neither requires additional hardware devices nor causes system instability, compared with MTD and watermarking. By elaborately constructing the encoding vector, the proposed smart-inverter-based meter encoding can mislead the attacker's SE while being hidden from alert attackers. In addition, by utilizing the topology of radial distribution systems, the proposed encoding scheme encodes fewer meters than current schemes when protecting the same number of buses, which decreases the encoding cost. Simulation results from the IEEE 69-bus distribution system demonstrate that the proposed meter encoding scheme can mislead the attacker's state estimation on all the downstream buses of an encoded bus without arousing the attacker's suspicion. FDI attacks constructed based on the misled estimated states are highly possible to trigger the defender's BDD alarm

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering