Robust Image Watermarking Using QR Factorization In Wavelet Domain

A robust blind image watermarking algorithm in wavelet transform domain (WT) based on QR factorization, and quantization index modulation (QIM) technique is presented for legal protection of digital images. The host image is decomposed into wavelet subbands, and then the approximation subband is QR factorized. The secret watermark bit is embedded into the R vector in QR using QIM. The experimental results show that the proposed algorithm preserves the high perceptual quality. It also sustains against JPEG compression, and other image processing attacks. The comparison analysis demonstrates the proposed scheme has better performance in imperceptibility and robustness than the previously reported watermarking algorithms

A performance evaluation of transform domain algorithm in watermarking based on different levels of sub-bands of discrete wavelet transform

This study proposed to evaluate the performance of transform domain algorithms in watermarking based on different level of sub bands of discrete wavelet transform (DWT) that can be used to overcome illegal copy, manipulation, distribution and reproduction of image on the network by implementing watermarking algorithm based on different levels that is low-level (LL), low- high (LH), high-low (HL), High-high (HH) and singular value decomposition (SVD) on image for their robustness, and imperceptibility. Moreover, two parameters were applied to evaluate the performance. That is, peak signal to noise ratio (PSNR) is used to measure imperceptibility of the digital image while normalize cross correlation (NCC) is applied to measure the robustness of digital image. The performance evaluations of the proposed method were implemented in MATLAB. The results showed that the described method imperceptible of HL level sub-bands than other levels of sub-bands. While the robustness is achieved by all levels sub-bands

Audio watermarking techniques using singular value decomposition

In an increasingly digital world, proving ownership of files is more and more difficult. For audio files, many schemes have been put into place to attempt to protect the rights of the digital content owners. In general, these techniques fall under the classification of Digital Rights Management (DRM). Audio watermarking is one of the less invasive schemes which embeds security into the data itself instead of in an outside layer meant to encapsulate and protect the data. There are many domains in which an audio watermark can be applied. The simplest is that of the time domain; often, however, other domains may be more desirable due to greater imperceptibility and robustness to attack. Common domains include the frequency domain, or domains similar to frequency through functions such as the Wavelet Transform. One domain of particular interest is that of the Singular Value Decomposition. The goal of this thesis is to propose and test many different watermarking schemes as well as test an existing watermarking scheme operating in the SVD domain in order to assess the viability of the SVD as a watermarking carrier domain. Different carrier matrices as well as bit embedding methods are explored. The use of a standard set of audio files was used to help test the systems; a standard set of watermarking tests was unavailable, so a comparable test bed was implemented and utilized

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Robust digital watermarking techniques for multimedia protection

The growing problem of the unauthorized reproduction of digital multimedia data such as movies, television broadcasts, and similar digital products has triggered worldwide efforts to identify and protect multimedia contents. Digital watermarking technology provides law enforcement officials with a forensic tool for tracing and catching pirates. Watermarking refers to the process of adding a structure called a watermark to an original data object, which includes digital images, video, audio, maps, text messages, and 3D graphics. Such a watermark can be used for several purposes including copyright protection, fingerprinting, copy protection, broadcast monitoring, data authentication, indexing, and medical safety. The proposed thesis addresses the problem of multimedia protection and consists of three parts. In the first part, we propose new image watermarking algorithms that are robust against a wide range of intentional and geometric attacks, flexible in data embedding, and computationally fast. The core idea behind our proposed watermarking schemes is to use transforms that have different properties which can effectively match various aspects of the signal's frequencies. We embed the watermark many times in all the frequencies to provide better robustness against attacks and increase the difficulty of destroying the watermark. The second part of the thesis is devoted to a joint exploitation of the geometry and topology of 3D objects and its subsequent application to 3D watermarking. The key idea consists of capturing the geometric structure of a 3D mesh in the spectral domain by computing the eigen-decomposition of the mesh Laplacian matrix. We also use the fact that the global shape features of a 3D model may be reconstructed using small low-frequency spectral coefficients. The eigen-analysis of the mesh Laplacian matrix is, however, prohibitively expensive. To lift this limitation, we first partition the 3D mesh into smaller 3D sub-meshes, and then we repeat the watermark embedding process as much as possible in the spectral coefficients of the compressed 3D sub-meshes. The visual error of the watermarked 3D model is evaluated by computing a nonlinear visual error metric between the original 3D model and the watermarked model obtained by our proposed algorithm. The third part of the thesis is devoted to video watermarking. We propose robust, hybrid scene-based MPEG video watermarking techniques based on a high-order tensor singular value decomposition of the video image sequences. The key idea behind our approaches is to use the scene change analysis to embed the watermark repeatedly in a fixed number of the intra-frames. These intra-frames are represented as 3D tensors with two dimensions in space and one dimension in time. We embed the watermark information in the singular values of these high-order tensors, which have good stability and represent the video properties. Illustration of numerical experiments with synthetic and real data are provided to demonstrate the potential and the much improved performance of the proposed algorithms in multimedia watermarking

Information Analysis for Steganography and Steganalysis in 3D Polygonal Meshes

Information hiding, which embeds a watermark/message over a cover signal, has recently found extensive applications in, for example, copyright protection, content authentication and covert communication. It has been widely considered as an appealing technology to complement conventional cryptographic processes in the field of multimedia security by embedding information into the signal being protected. Generally, information hiding can be classified into two categories: steganography and watermarking. While steganography attempts to embed as much information as possible into a cover signal, watermarking tries to emphasize the robustness of the embedded information at the expense of embedding capacity. In contrast to information hiding, steganalysis aims at detecting whether a given medium has hidden message in it, and, if possible, recover that hidden message. It can be used to measure the security performance of information hiding techniques, meaning a steganalysis resistant steganographic/watermarking method should be imperceptible not only to Human Vision Systems (HVS), but also to intelligent analysis. As yet, 3D information hiding and steganalysis has received relatively less attention compared to image information hiding, despite the proliferation of 3D computer graphics models which are fairly promising information carriers. This thesis focuses on this relatively neglected research area and has the following primary objectives: 1) to investigate the trade-off between embedding capacity and distortion by considering the correlation between spatial and normal/curvature noise in triangle meshes; 2) to design satisfactory 3D steganographic algorithms, taking into account this trade-off; 3) to design robust 3D watermarking algorithms; 4) to propose a steganalysis framework for detecting the existence of the hidden information in 3D models and introduce a universal 3D steganalytic method under this framework. %and demonstrate the performance of the proposed steganalysis by testing it against six well-known 3D steganographic/watermarking methods. The thesis is organized as follows. Chapter 1 describes in detail the background relating to information hiding and steganalysis, as well as the research problems this thesis will be studying. Chapter 2 conducts a survey on the previous information hiding techniques for digital images, 3D models and other medium and also on image steganalysis algorithms. Motivated by the observation that the knowledge of the spatial accuracy of the mesh vertices does not easily translate into information related to the accuracy of other visually important mesh attributes such as normals, Chapters 3 and 4 investigate the impact of modifying vertex coordinates of 3D triangle models on the mesh normals. Chapter 3 presents the results of an empirical investigation, whereas Chapter 4 presents the results of a theoretical study. Based on these results, a high-capacity 3D steganographic algorithm capable of controlling embedding distortion is also presented in Chapter 4. In addition to normal information, several mesh interrogation, processing and rendering algorithms make direct or indirect use of curvature information. Motivated by this, Chapter 5 studies the relation between Discrete Gaussian Curvature (DGC) degradation and vertex coordinate modifications. Chapter 6 proposes a robust watermarking algorithm for 3D polygonal models, based on modifying the histogram of the distances from the model vertices to a point in 3D space. That point is determined by applying Principal Component Analysis (PCA) to the cover model. The use of PCA makes the watermarking method robust against common 3D operations, such as rotation, translation and vertex reordering. In addition, Chapter 6 develops a 3D specific steganalytic algorithm to detect the existence of the hidden messages embedded by one well-known watermarking method. By contrast, the focus of Chapter 7 will be on developing a 3D watermarking algorithm that is resistant to mesh editing or deformation attacks that change the global shape of the mesh. By adopting a framework which has been successfully developed for image steganalysis, Chapter 8 designs a 3D steganalysis method to detect the existence of messages hidden in 3D models with existing steganographic and watermarking algorithms. The efficiency of this steganalytic algorithm has been evaluated on five state-of-the-art 3D watermarking/steganographic methods. Moreover, being a universal steganalytic algorithm can be used as a benchmark for measuring the anti-steganalysis performance of other existing and most importantly future watermarking/steganographic algorithms. Chapter 9 concludes this thesis and also suggests some potential directions for future work

Digital watermarking in medical images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/12/2005.This thesis addresses authenticity and integrity of medical images using watermarking. Hospital Information Systems (HIS), Radiology Information Systems (RIS) and Picture Archiving and Communication Systems (P ACS) now form the information infrastructure for today's healthcare as these provide new ways to store, access and distribute medical data that also involve some security risk. Watermarking can be seen as an additional tool for security measures. As the medical tradition is very strict with the quality of biomedical images, the watermarking method must be reversible or if not, region of Interest (ROI) needs to be defined and left intact. Watermarking should also serve as an integrity control and should be able to authenticate the medical image. Three watermarking techniques were proposed. First, Strict Authentication Watermarking (SAW) embeds the digital signature of the image in the ROI and the image can be reverted back to its original value bit by bit if required. Second, Strict Authentication Watermarking with JPEG Compression (SAW-JPEG) uses the same principal as SAW, but is able to survive some degree of JPEG compression. Third, Authentication Watermarking with Tamper Detection and Recovery (AW-TDR) is able to localise tampering, whilst simultaneously reconstructing the original image

Watermarking Federated Deep Neural Network Models

Training DNN models is expensive in terms of computational power, collection of a large amount of labeled data, and human expertise. Thus, DNN models constitute intellectual property (IP) and business value for their owners. Embedding digital watermarks into model training allows model owners to later demonstrate ownership, which can effectively protect the IP of their models. Recently, federated learning has been proposed as a new framework for machine learning development, which distributes the training of a global deep neural network (DNN) model over a large number of participants. Therefore, federated learning is advantageous than traditional DNN training in terms of data privacy, computational resources and a distributed optimization. However, there is no prior work investigating a solution for watermarking federated DNN models. The main challenge is that the distributed training causes the separation of training data (on participants' side) and watermark set (on aggregator's side), which does not satisfy the condition of traditional watermarking techniques that requires both training data and watermark set to be stored in the same place. In this thesis, we introduce two novel federated watermarking approaches which can embed watermark into federated DNN models by backdooring with low communication and computational overhead. In our approaches, the embedding of watermark is completed by the aggregator while the training is done by participants. We prove that our approaches embed a watermark with a high accuracy (100%) while keeping the functionality of the model. Moreover, the embedded watermarks in DNN models are resistant to post-processing techniques. We also propose a new watermark generation method and evaluate its efficacy in terms of unremovability, model utility and computational cost aspects