Ahmad T. Al-Taani

Bio: Ahmad T. Al-Taani is an academic researcher from Yarmouk University. The author has contributed to research in topics: Automatic summarization & Steganography. The author has an hindex of 13, co-authored 32 publications receiving 481 citations.

A Novel Steganographic Method for Gray-Level Images

Abstract: — In this work we propose a novel Steganographic method for hiding information within the spatial domain of the gray scale image The proposed approach works by dividing the cover into blocks of equal sizes and then embeds the message in the edge of the block depending on the number of ones in left four bits of the pixel The proposed approach is tested on a database consists of 100 different images Experimental results, compared with other methods, showed that the proposed approach hide more large information and gave a good visual quality stego-image that can be seen by human eyes Keywords — Data Embedding, Cryptography, Watermarking, Steganography, Least Significant Bit, Information Hiding I INTRODUCTION ATA hiding became an important field as the use of the Internet became popular Data hiding is a young field and it is growing in an exponential rate [1],[2] Steganography is used to hide information inside other As derived from Greek, the word steganography literally means “Covered Writing” Steganography is the art and science of communicating in a way, which hides the existence of the communication, or it is the art of hiding information in ways that prevent the detection of hidden messages [3] The main purpose of steganography is to hide a message in another one in a way that prevents any attacker to detect or notice the hidden message The aim of this work is to develop a new method for hiding message in gray-scale images, mainly embedding text data in digital images In this work we present an efficient Steganographic approach for hiding information within a gray scale image We have compared the new method with two well-known methods, PVD and GLM methods Our results show the effectiveness of the proposed method compared with the other methods The rest of this paper is organized as follows Section 2 introduces some important issues of Steganography and Data Hiding Methods Section 3 discuses related work in the field of data hiding Methods and materials are discussed in Section 4 Section 5 introduces the experimental results of the proposed method Conclusions and future work are presented in section 6

Data Embedding Based on Better Use of Bits in Image Pixels

Abstract: In this study, a novel approach of image embedding is introduced. The proposed method consists of three main steps. First, the edge of the image is detected using Sobel mask filters. Second, the least significant bit LSB of each pixel is used. Finally, a gray level connectivity is applied using a fuzzy approach and the ASCII code is used for information hiding. The prior bit of the LSB represents the edged image after gray level connectivity, and the remaining six bits represent the original image with very little difference in contrast. The proposed method embeds three images in one image and includes, as a special case of data embedding, information hiding, identifying and authenticating text embedded within the digital images. Image embedding method is considered to be one of the good compression methods, in terms of reserving memory space. Moreover, information hiding within digital image can be used for security information transfer. The creation and extraction of three embedded images, and hiding text information is discussed and illustrated, in the following sections. Keywords—Image embedding, Edge detection, gray level connectivity, information hiding, digital image compression.

A rule-based approach for tagging non-vocalized Arabic words.

Abstract: In this work, we present a tagging system which classifies the words in a non-vocalized Arabic text to their tags. The proposed tagging system passes through three levels of analysis. The first level is a lexical analyzer that composed of a lexicon containing all fixed words and particles such as prepositions and pronouns. The second level is a morphological analyzer which relies on word structure using patterns and affixes to determine word class. The third level is a syntax analyzer or a grammatical tagging which relies on the process of assigning grammatical tags to words based on their context or the position of the word in the sentence. The syntax analyzer level consists of two stages: the first stage depends on specific keywords that inform the tag of the successive word, the second stage is the reversed parsing technique which scans the available grammars of Arabic language to get the class of a single ambiguity word in the sentence. We have tested the proposed system on a corpus consists of 2355 words. Experimental results showed that the proposed system achieved a rate of success approaching 94% of the total number of words in the sample used in the study.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Automatic text summarization: A comprehensive survey

Abstract: Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.

A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image

Abstract: Image Steganography is a thriving research area of information security where secret data is embedded in images to hide its existence while getting the minimum possible statistical detectability. This paper proposes a novel magic least significant bit substitution method (M-LSB-SM) for RGB images. The proposed method is based on the achromatic component (I-plane) of the hue-saturation-intensity (HSI) color model and multi-level encryption (MLE) in the spatial domain. The input image is transposed and converted into an HSI color space. The I-plane is divided into four sub-images of equal size, rotating each sub-image with a different angle using a secret key. The secret information is divided into four blocks, which are then encrypted using an MLE algorithm (MLEA). Each sub-block of the message is embedded into one of the rotated sub-images based on a specific pattern using magic LSB substitution. Experimental results validate that the proposed method not only enhances the visual quality of stego images but also provides good imperceptibility and multiple security levels as compared to several existing prominent methods.