A Comprehensive Review on Digital Image Watermarking

The advent of the Internet led to the easy availability of digital data like images, audio, and video. Easy access to multimedia gives rise to the issues such as content authentication, security, copyright protection, and ownership identification. Here, we discuss the concept of digital image watermarking with a focus on the technique used in image watermark embedding and extraction of the watermark. The detailed classification along with the basic characteristics, namely visual imperceptibility, robustness, capacity, security of digital watermarking is also presented in this work. Further, we have also discussed the recent application areas of digital watermarking such as healthcare, remote education, electronic voting systems, and the military. The robustness is evaluated by examining the effect of image processing attacks on the signed content and the watermark recoverability. The authors believe that the comprehensive survey presented in this paper will help the new researchers to gather knowledge in this domain. Further, the comparative analysis can enkindle ideas to improve upon the already mentioned techniques

Robust Watermarking Schemes for Digital Images

With the rapid development of multimedia and the widespread distribution of digital data over the internet networks, it has become easy to obtain the intellectual properties. Consequently, the multimedia owners need more than ever before to protect their data and to prevent their unauthorized use. Digital watermarking has been proposed as an effective method for copyright protection and an unauthorized manipulation of the multimedia. Watermarking refers to the process of embedding an identification code or some other information called watermark into digital multimedia without affecting the visual quality of the host multimedia. Such a watermark can be used for several purposes including copyright protection and fingerprinting of the multimedia for tracing and data authentication. The goal in a watermarking scheme is to embed a watermark that is robust against various types of attacks while preserving the perceptual quality of the cover image. A variety of schemes have been proposed in the literature to achieve these goals for watermarking of images. These schemes either provide good imperceptibility of the watermark without sufficient resilience to certain types of attacks or provide good robustness against attacks at the expense of degraded perceptual quality of the cover images. The objective of this work is to develop image watermarking schemes with performance that is superior to those of existing schemes in terms of their robustness against various types of attacks while preserving the perceptual of the cover image. In this thesis, two new digital image watermarking schemes are proposed. In the first scheme, an Arnold transform integrated DCT-SVD based image watermarking scheme is developed. The main idea in this scheme is to improve the robustness of the watermarking further by scrambling the watermark data using the Arnold transform while still preserving the good perceptibility of the watermarked image furnished by a DCT-SVD based embedding. Also, it is shown that considerable savings in the computation time to recover the original watermark image can be provided by using the anti-Arnold transform in the watermark extraction process. In the second scheme, a DWT-SVD digital image watermarking scheme that makes use of visual cryptography to embed and extract a binary watermark image is developed. The use of visual cryptography in the proposed watermarking scheme is intended to provide improved robustness against attacks along with furnishing security to the content of the embedded data. Extensive experiments are conducted throughout this investigation in order to examine the performance of the proposed watermarking schemes. It is shown that the two proposed watermarking schemes developed in this thesis provide a performance superior to that of the existing schemes in terms of robustness against various types of attacks while preserving the perceptual quality of the cover image

An improved image steganography scheme based on distinction grade value and secret message encryption

Steganography is an emerging and greatly demanding technique for secure information communication over the internet using a secret cover object. It can be used for a wide range of applications such as safe circulation of secret data in intelligence, industry, health care, habitat, online voting, mobile banking and military. Commonly, digital images are used as covers for the steganography owing to their redundancy in the representation, making them hidden to the intruders, hackers, adversaries, unauthorized users. Still, any steganography system launched over the Internet can be cracked upon recognizing the stego cover. Thus, the undetectability that involves data imperceptibility or concealment and security is the significant trait of any steganography system. Presently, the design and development of an effective image steganography system are facing several challenges including low capacity, poor robustness and imperceptibility. To surmount such limitations, it is important to improve the capacity and security of the steganography system while maintaining a high signal-to-noise ratio (PSNR). Based on these factors, this study is aimed to design and develop a distinction grade value (DGV) method to effectively embed the secret data into a cover image for achieving a robust steganography scheme. The design and implementation of the proposed scheme involved three phases. First, a new encryption method called the shuffle the segments of secret message (SSSM) was incorporated with an enhanced Huffman compression algorithm to improve the text security and payload capacity of the scheme. Second, the Fibonacci-based image transformation decomposition method was used to extend the pixel's bit from 8 to 12 for improving the robustness of the scheme. Third, an improved embedding method was utilized by integrating a random block/pixel selection with the DGV and implicit secret key generation for enhancing the imperceptibility of the scheme. The performance of the proposed scheme was assessed experimentally to determine the imperceptibility, security, robustness and capacity. The standard USC-SIPI images dataset were used as the benchmarking for the performance evaluation and comparison of the proposed scheme with the previous works. The resistance of the proposed scheme was tested against the statistical, X2 , Histogram and non-structural steganalysis detection attacks. The obtained PSNR values revealed the accomplishment of higher imperceptibility and security by the proposed DGV scheme while a higher capacity compared to previous works. In short, the proposed steganography scheme outperformed the commercially available data hiding schemes, thereby resolved the existing issues

Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods

The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33%, was then enhanced to 96.88%. The accuracy of VGG19, which was 77.33%, was then enhanced to 95.31%, and DenseNet201, which was originally 82.66%, was then enhanced to 93.75%. The models' accuracy percentage engagement change is 13.53%, 23.25%, and 23.25%, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch

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

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification

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

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Preserving privacy in edge computing

Edge computing or fog computing enables realtime services to smart application users by storing data and services at the edge of the networks. Edge devices in the edge computing handle data storage and service provisioning. Therefore, edge computing has become a  new norm for several delay-sensitive smart applications such as automated vehicles, ambient-assisted living, emergency response services, precision agriculture, and smart electricity grids. Despite having great potential, privacy threats are the main barriers to the success of edge computing. Attackers can leak private or sensitive information of data owners and modify service-related data for hampering service provisioning in edge computing-based smart applications. This research takes privacy issues of heterogeneous smart application data into account that are stored in edge data centers. From there, this study focuses on the development of privacy-preserving models for user-generated smart application data in edge computing and edge service-related data, such as Quality-of-Service (QoS) data, for ensuring unbiased service provisioning. We begin with developing privacy-preserving techniques for user data generated by smart applications using steganography that is one of the data hiding techniques. In steganography, user sensitive information is hidden within nonsensitive information of data before outsourcing smart application data, and stego data are produced for storing in the edge data center. A steganography approach must be reversible or lossless to be useful in privacy-preserving techniques. In this research, we focus on numerical (sensor data) and textual (DNA sequence and text) data steganography. Existing steganography approaches for numerical data are irreversible. Hence, we introduce a lossless or reversible numerical data steganography approach using Error Correcting Codes (ECC). Modern lossless steganography approaches for text data steganography are mainly application-specific and lacks imperceptibility, and DNA steganography requires reference DNA sequence for the reconstruction of the original DNA sequence. Therefore, we present the first blind and lossless DNA sequence steganography approach based on the nucleotide substitution method in this study. In addition, a text steganography method is proposed that using invisible character and compression based encoding for ensuring reversibility and higher imperceptibility.  Different experiments are conducted to demonstrate the justification of our proposed methods in these studies. The searching capability of the stored stego data is challenged in the edge data center without disclosing sensitive information. We present a privacy-preserving search framework for stego data on the edge data center that includes two methods. In the first method, we present a keyword-based privacy-preserving search method that allows a user to send a search query as a hash string. However, this method does not support the range query. Therefore, we develop a range search method on stego data using an order-preserving encryption (OPE) scheme. In both cases, the search service provider retrieves corresponding stego data without revealing any sensitive information. Several experiments are conducted for evaluating the performance of the framework. Finally, we present a privacy-preserving service computation framework using Fully Homomorphic Encryption (FHE) based cryptosystem for ensuring the service provider's privacy during service selection and composition. Our contributions are two folds. First, we introduce a privacy-preserving service selection model based on encrypted Quality-of-Service (QoS) values of edge services for ensuring privacy. QoS values are encrypted using FHE. A distributed computation model for service selection using MapReduce is designed for improving efficiency. Second, we develop a composition model for edge services based on the functional relationship among edge services for optimizing the service selection process. Various experiments are performed in both centralized and distributed computing environments to evaluate the performance of the proposed framework using a synthetic QoS dataset