Reversible Data Hiding Scheme with High Embedding Capacity Using Semi-Indicator-Free Strategy

A novel reversible data-hiding scheme is proposed to embed secret data into a side-matched-vector-quantization- (SMVQ-) compressed image and achieve lossless reconstruction of a vector-quantization- (VQ-) compressed image. The rather random distributed histogram of a VQ-compressed image can be relocated to locations close to zero by SMVQ prediction. With this strategy, fewer bits can be utilized to encode SMVQ indices with very small values. Moreover, no indicator is required to encode these indices, which yields extrahiding space to hide secret data. Hence, high embedding capacity and low bit rate scenarios are deposited. More specifically, in terms of the embedding rate, the bit rate, and the embedding capacity, experimental results show that the performance of the proposed scheme is superior to those of the former data hiding schemes for VQ-based, VQ/SMVQ-based, and search-order-coding- (SOC-) based compressed images

Application of Stochastic Diffusion for Hiding High Fidelity Encrypted Images

Cryptography coupled with information hiding has received increased attention in recent years and has become a major research theme because of the importance of protecting encrypted information in any Electronic Data Interchange system in a way that is both discrete and covert. One of the essential limitations in any cryptography system is that the encrypted data provides an indication on its importance which arouses suspicion and makes it vulnerable to attack. Information hiding of Steganography provides a potential solution to this issue by making the data imperceptible, the security of the hidden information being a threat only if its existence is detected through Steganalysis. This paper focuses on a study methods for hiding encrypted information, specifically, methods that encrypt data before embedding in host data where the ‘data’ is in the form of a full colour digital image. Such methods provide a greater level of data security especially when the information is to be submitted over the Internet, for example, since a potential attacker needs to first detect, then extract and then decrypt the embedded data in order to recover the original information. After providing an extensive survey of the current methods available, we present a new method of encrypting and then hiding full colour images in three full colour host images with out loss of fidelity following data extraction and decryption. The application of this technique, which is based on a technique called ‘Stochastic Diffusion’ are wide ranging and include covert image information interchange, digital image authentication, video authentication, copyright protection and digital rights management of image data in general

A User Oriented Image Retrieval System using Halftoning BBTC

The objective of this paper is to develop a system for content based image retrieval (CBIR) by accomplishing the benefits of low complexity Ordered Dither Block Truncation Coding based on half toning technique for the generation of image content descriptor. In the encoding step ODBTC compresses an image block into corresponding quantizes and bitmap image. Two image features are proposed to index an image namely co-occurrence features and bitmap patterns which are generated using ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two quantizes and bitmap respectively by including visual codebooks. The proposed system based on block truncation coding image retrieval method is not only convenient for an image compression but it also satisfy the demands of users by offering effective descriptor to index images in CBIR system

Encryption and Decryption of Images with Pixel Data Modification Using Hand Gesture Passcodes

To ensure data security and safeguard sensitive information in society, image encryption and decryption as well as pixel data modifications, are essential. To avoid misuse and preserve trust in our digital environment, it is crucial to use these technologies responsibly and ethically. So, to overcome some of the issues, the authors designed a way to modify pixel data that would hold the hidden information. The objective of this work is to change the pixel values in a way that can be used to store information about black and white image pixel data. Prior to encryption and decryption, by using Python we were able to construct a passcode with hand gestures in the air, then encrypt it without any data loss. It concentrates on keeping track of simply two pixel values. Thus, pixel values are slightly changed to ensure the masked image is not misleading. Considering that the RGB values are at their border values of 254, 255 the test cases of masking overcome issues with the corner values susceptibility

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

Improved Encoding for Compressed Textures

For the past few decades, graphics hardware has supported mapping a two dimensional image, or texture, onto a three dimensional surface to add detail during rendering. The complexity of modern applications using interactive graphics hardware have created an explosion of the amount of data needed to represent these images. In order to alleviate the amount of memory required to store and transmit textures, graphics hardware manufacturers have introduced hardware decompression units into the texturing pipeline. Textures may now be stored as compressed in memory and decoded at run-time in order to access the pixel data. In order to encode images to be used with these hardware features, many compression algorithms are run offline as a preprocessing step, often times the most time-consuming step in the asset preparation pipeline. This research presents several techniques to quickly serve compressed texture data. With the goal of interactive compression rates while maintaining compression quality, three algorithms are presented in the class of endpoint compression formats. The first uses intensity dilation to estimate compression parameters for low-frequency signal-modulated compressed textures and offers up to a 3X improvement in compression speed. The second, FasTC, shows that by estimating the final compression parameters, partition-based formats can choose an approximate partitioning and offer orders of magnitude faster encoding speed. The third, SegTC, shows additional improvement over selecting a partitioning by using a global segmentation to find the boundaries between image features. This segmentation offers an additional 2X improvement over FasTC while maintaining similar compressed quality. Also presented is a case study in using texture compression to benefit two dimensional concave path rendering. Compressing pixel coverage textures used for compositing yields both an increase in rendering speed and a decrease in storage overhead. Additionally an algorithm is presented that uses a single layer of indirection to adaptively select the block size compressed for each texture, giving a 2X increase in compression ratio for textures of mixed detail. Finally, a texture storage representation that is decoded at runtime on the GPU is presented. The decoded texture is still compressed for graphics hardware but uses 2X fewer bytes for storage and network bandwidth.Doctor of Philosoph

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

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field