ABSTRACT

Abstract:In this research, we propose a new approach to increase the capacity and enhance the reliability of hiding voice data in Arabic text. Using Kashida to hide bits in Arabic text is one of the most promising approaches in steganography. Unfortunately, ignoring the original Kashida in the cover text may affect the results significantly and produce inaccurate results in the extraction process. In this study, we propose tremendous improvements to the Kashida method by considering original Kashida(-) in the cover text, error-detection using Cyclic Redundancy Check (CRC) and hiding bit using the "La" word. Moreover, hiding voice files within Arabic text is considered. The proposed approach is compared with the most related approaches in terms of capacity, security and reliability. Not only are the findings of the paper promising, they also overcome the limitations of other approaches.

In CBIR (content-based image retrieval) features are extracted based on color, texture, and shape. There are many factors affecting the accuracy (precision) of retrieval such as number of features, type of features (local or global), color model, and distance measure. In this paper, a two phases approach to retrieve similar images from data set based on color and texture is proposed. In the first phase, global color histogram is utilized with HSV (hue, saturation, and value) color model and an automatic cropping technique is proposed to accelerate the process of features extraction and enhances the accuracy of retrieval. Joint histogram and GLCM (gray-level co-occurrence matric) are deployed in phase two. In this phase, color features and texture features are combined to enhance the accuracy of retrieval. Finally, a new way of using K-means as clustering algorithm is proposed to classify and retrieve images. Two experiments are conducted using WANG database. WANG database consists of 10 different classes each with 100 images. Results of comparing the proposed approach with the most relevant approaches are promising.

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