High Capacity Image Steganography Technique based on LSB Substitution Method

Steganography is the art of hiding important data by embedding secret message within other file. The least significant bit (LSB) is one of the most important considerations when one want to solve such problems. LSB substitution method exchanges some LSB of the cover-image with the secret data. In this paper, data hiding method using simple LSB substitution is proposed. The main goal of the presented method is to increase the embedding capacity and improve the image quality of the stego-image. The experimental results show better performance of the proposed method compared to the corresponding methods, in terms of PSNR and the capacity. The effectiveness of the model is estimated from the viewpoint of both the amount of data hidden and the image quality of the cover image

A Soil Moisture Map Generated from Satellite Data by Using Domains of Attraction in Neural Networks

A learning method using domains of attraction in three-layered neural networks is proposed. The method is a combination of output error minimization learning with maximization learning of domains of attraction in one-layered perceptrons. To simplify the structure of the network, a successive learning technique is employed for hidden units. Domains of attraction in the network is derived by restricting output conditions at the hidden layer for training input data. A three-layered neural network is determined by this method using a training set which consists of satellite observation data and soil moisture data surveyed in some places. The estimation of soil moisture at all places corresponding to satellite data is carried out based on the derived domains of attraction in the network. Keyword:learning method, domain of attraction, satellite data, soil moisture data 1 INTRODUCTION The back propagation method (BPM) has been used well for learning multi-layered neural networks. Until now, ..

Multi-Service Data Security Algorithm Based on Substitution Ciphers and Hadamard Transform

Cryptography has attracted much attention of many researchers, especially in protecting signal information as a part of an information security system. Several types of research have been done in developing too many approaches with the main focus on confidentiality of the Cryptography framework. “Golden” Matrices Cryptography (GC) has demonstrated superior performance compared to the state-of-the-art algorithms in terms of the computational time. However, it sometimes shows unsecured performance due to easily guess the encryption key. In this paper, an approach to overcome the main limitation of GC by a combination of NBlocks Random Matrix ASCII Ciphers (NBRMAC), Hadamard product of Golden matrices and Hadamard Transform is proposed. Message Authentication Code (MAC) will be considered in the process to provide authentication and integrity. The proposed scheme is implemented out of the seven stages, starting with encoding plaintext by (NBRMAC) , applied HMAC, key generation, encryption stage, decryption stage, matching HMAC, and finally the decoding to obtain the original data at the end of communication

Scalable Algorithms for Missing Value Imputation

Statistical Imputation Techniques have been proposed mainly with the aim of predicting the missing values in the incomplete sets as an essential step in any data analysis framework. K-means-based Imputation, as a representative statistical imputation method, has been producing satisfied results in terms of effectiveness and efficiency in handling popular and freely available data set (e.g., Bupa, Breast Cancer, Pima, etc.). The main idea of K-means based methods is to impute the missing value relying on the prototypes of the representative class and the similarity of the data. However, such kinds of methods share the same limitations of the K-means as data mining technique. In this paper and motivated by such drawbacks, we introduce simple and efficient imputation methods based on K-means to deal with the missing data from various classes of data sets. Our proposed methods give higher accuracy than the one given by the standard K-means

Secure Watermarking Algorithm based on DNA Sequence Using DWT-SVD

Due to the continuous development of information technology and the widespread use of digital media, such as text, videos, and digital images, the digital watermark has become one of the most important methods to protect digital media from tampering. DNA sequence-based image has been used as a biometric watermark for copyright protection and authentication, where the DNA sequence is a unique biometric feature and difficult to copy or duplicate. We have been presented a watermarking algorithm that based on Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD), in order to achieve imperceptibility. The quality of the watermarked image has been measured using peak signal to noise ratio (PSNR), at different DNA sequence-based images. This algorithm can be used for authentication and data hiding purposes

Some Ad Hoc Network Characteristics Effects on the Performance of On-Demand Routing Protocols

AMobile Ad-Hoc Network (MANET) is a collection of wireless nodes that can be setup dynamically anywhere and anytime without using any pre-existing network infrastructure. Each node can communicate with another node that is immediately within their radio range. Traditionally Ad-Hoc routing protocols are typically used to deal with the dynamic nature of these networks and can survive rapid changes in the network topology. These protocols typically suffer from a number of shortcomings, such as high routing overhead and limited scalability. This motivates the work presented in this paper, which provides a comparison of AODV and DSR protocols inMobile Ad-Hoc network. Our experimental results show that the routing path is changed by using the Critical Transmission Range. A comparison analysis of the topology changes, transmission range and hop count is presented