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

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

A forensics software toolkit for DNA steganalysis.

Recent advances in genetic engineering have allowed the insertion of artificial DNA strands into the living cells of organisms. Several methods have been developed to insert information into a DNA sequence for the purpose of data storage, watermarking, or communication of secret messages. The ability to detect, extract, and decode messages from DNA is important for forensic data collection and for data security. We have developed a software toolkit that is able to detect the presence of a hidden message within a DNA sequence, extract that message, and then decode it. The toolkit is able to detect, extract, and decode messages that have been encoded with a variety of different coding schemes. The goal of this project is to enable our software toolkit to determine with which coding scheme a message has been encoded in DNA and then to decode it. The software package is able to decode messages that have been encoded with every variation of most of the coding schemes described in this document. The software toolkit has two different options for decoding that can be selected by the user. The first is a frequency analysis approach that is very commonly used in cryptanalysis. This approach is very fast, but is unable to decode messages shorter than 200 words accurately. The second option is using a Genetic Algorithm (GA) in combination with a Wisdom of Artificial Crowds (WoAC) technique. This approach is very time consuming, but can decode shorter messages with much higher accuracy

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field