Abdelmgeid A. Ali

New Text Steganography Technique by using Mixed-Case Font

One of the most important techniques of information hiding is a steganography. Steganography is the art of hiding information within other information or carriers (i.e. text, image, video or audio) in such a way that it is hard or even impossible to tell that it is there. In This paper, a new text steganography method is proposed. The main goal of this method is to use text file as a carrier for the secret message data in such a manner that the resultant text file attracts no attention and hold the same meaning of the original file.

Enhancing SMM Image Steganography Method by using LSBraille Image Steganography Method (SMMWB; Secret Message Matching With Braille)

ormation hiding techniques play a vital role in the recent years. Steganography is one of the important information hiding techniques which hides the existence of the message in the cover file. It gained importance in the past few years due to the increasing need for providing secrecy in an open environment like the internet. It can be defined as the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the internet. Steganography is used to conceal the information so that no one can sense its existence. It has many technical challenges such as high hiding capacity and imperceptibility. The current technique proposed in this paper is based on previously proposed SMM image steganography method (Image Steganography By Matching Secret Message With Pixels of Cover Image or, Secret Message Matching) combined with previously proposed LSBraille image steganography method (Image Steganography Method By Using Braille Method of Blind People). The proposed method provides more MHC (Maximum Hiding Capacity) than the previously proposed method SMM combined with an excellent PSNR (Peak Signal to Noise Ratio).

Ear recognition using local binary patterns: A comparative experimental study

Abstract Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions; namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%; while, their performance faces difficulties when the level of distortions increases. Moreover, it is noted that the tested LBP variants achieve almost close performance on ear recognition. Thus, further studies on other applications are needed to verify this close performance. We believe that the presented study has significant insights and can benefit researchers in choosing between LBP variants as well as acting as a connection between previous studies and future work in utilizing LBP-like features in ear recognition systems.

Using a Business Activity Monitoring and SOA for a Real-Time ETL

Data that is obtained from various information sources needs excessively handling for managing, analyzing and monitoring. Data warehouse consolidates data coming from different data sources. Data warehousing technology has made a huge effect in the world of business; it transforms information into data that helps analysts to make strategic decisions. A real-time data warehouse is used same purpose as data warehouse, in addition to these, data streams into real time data warehouse on time. In this paper, propose a real-time framework to support the up-to-date process. Our framework is based on using a Business Activity Monitoring (BAM) that provides a real-time business intelligence by capturing data as it flows through a business system. Using service-oriented architecture (SOA) and Windows Communication Foundation (WCF) to capture a real-time data and load easily. By using BAM, it can monitor a business process in real time.

Ear Biometric Recognition Using Gradient-Based Feature Descriptors

Recently, intensive research efforts are conducted on the human ear as a promising biometric modality for identity recognition. However, one of the main challenges facing ear recognition systems is to find robust representation for the image information that is invariant to different imaging variations. Recent studies indicate that using the distribution of local intensity gradients or edge directions can better discriminate the shape and appearance of objects. Moreover, gradient-based features are robust to global and local intensity variations as well as noise and geometric transformation of images. This paper presents an ear biometric recognition approach based on the gradient-based features. To this end, four local feature extractors are investigated, namely: Histogram of Oriented Gradients (HOG), Weber Local Descriptor (WLD), Local Directional Patterns (LDP), and Local Optimal Oriented Patterns (LOOP). Extensive experiments are conducted for both identification and verification using the publicly available IIT Delhi-I, IIT Delhi-II, and AMI ear databases. The obtained results are encouraging, where the LOOP features excel in all cases achieving recognition rates of approximately 97%.

An Experimental Evaluation of Binary Feature Descriptors

Efficient and compact representation of local image patches in the form of features descriptors that are distinctive/robust as well as fast to compute and match is an essential and inevitable step for many computer vision applications. One category of these representations is the binary descriptors which have been shown to be successful alternatives providing similar performance to their floating-point counterparts while being efficient to compute and store. In this paper, a comprehensive performance evaluation of the current state-of-the-art binary descriptors; namely, BRIEF, ORB, BRISK, FREAK, and LATCH is presented in the context of image matching. This performance evaluation highlights several points regarding the performance characteristics of binary descriptors under various geometric and photometric transformations of images.

Digital Image Watermarking using MSLDIP (Modified Substitute Last Digit in Pixel)

improvements in Internet technologies and growing requests on online multimedia businesses have made digital copyrighting as a significant challenge for businesses that are related with online content distribution via diverse business models including pay-per-view, subscription, trading, etc. Copyright protection and the proof for rightful ownership are major issues related with the distribution of any digital images. Digital watermarking is one of the suggested solutions for copyright protection of multimedia data. This technique is better than Digital Signatures and other methods because it does not rise overhead. In this paper a new watermarking method based on spatial domain image steganography method called MSLDIP (Modified Substitute Last Digit in Pixel) is proposed. The main goal of this method is to hide the watermark in the pixels of digital image in such a manner that the human visual system is not able to distinguish between the cover image and the watermarked image. Also the experimental results showed that this method can be used effectively in the field of watermarking.