Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.

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With ceaseless utilization of web and other online advances, it has turned out to be amazingly simple to imitate, discuss, and convey digital material. Subsequently, confirmation and copyright assurance issues have been emerged. Text is the most widely used media of communication on the web as compared with images and videos. The significant part of books, daily papers, websites, commercial, research papers, reports, and numerous different archives are just the plain text. Therefore, copyrights protection of plain text is a critical issue that could not be accepted at all. This paper presents a concise analysis on information hiding techniques and their consequences. Various watermarking systems properties are highlighted along with different types of attacks and their possible defenses. Additionally, the current applications for text document watermarking and the reasons for the problems of text document watermarking are described. Finally, attacks characteristics are discussed including types of attacks, volumes, and nature on text. Current watermarking approaches are reviewed in depth, and consequently, their advantages and weaknesses are highlighted.

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ABSTRACT

The rapid growth of covert activities via communications network brought about an increasing need to provide an efficient method for data hiding to protect secret information from malicious attacks. One of the options is to combine two approaches, namely steganography and compression. However, its performance heavily relies on three major factors, payload, imperceptibility, and robustness, which are always in trade-offs. Thus, this study aims to hide a large amount of secret message inside a grayscale host image without sacrificing its quality and robustness. To realize the goal, a new two-tier data hiding technique is proposed that integrates an improved exploiting modification direction (EMD) method and Huffman coding. First, a secret message of an arbitrary plain text of characters is compressed and transformed into streams of bits; each character is compressed into a maximum of 5 bits per stream. The stream is then divided into two parts of different sizes of 3 and 2 bits. Subsequently, each part is transformed into its decimal value, which serves as a secret code. Second, a cover image is partitioned into groups of 5 pixels based on the original EMD method. Then, an enhancement is introduced by dividing the group into two parts, namely k 1 and k 2 , which consist of 3 and 2 pixels, respectively. Furthermore, several groups are randomly selected for embedding purposes to increase the security. Then, for each selected group, each part is embedded with its corresponding secret code by modifying one grayscale value at most to hide the code in a (2ki + 1)-ary notational system. The process is repeated until a stego-image is eventually produced. Finally, the χ 2 test, which is considered one of the most severe attacks, is applied against the stego-image to evaluate the performance of the proposed method in terms of its robustness. The test revealed that the proposed method is more robust than both least significant bit embedding and the original EMD. Additionally, in terms of imperceptibility and capacity, the experimental results have also shown that the proposed method outperformed both the well-known methods, namely original EMD and optimized EMD, with a peak signal-to-noise ratio of 55.92 dB and payload of 52,428 bytes.

Fractal image compression (FIC) is recognized as a NP-hard problem, and it suffers from a high number of mean square error (MSE) computations. In this paper, a two-phase algorithm was proposed to reduce the MSE computation of FIC. In the first phase, based on edge property, range and domains are arranged. In the second one, imperialist competitive algorithm (ICA) is used according to the classified blocks. For maintaining the quality of the retrieved image and accelerating algorithm operation, we divided the solutions into two groups: developed countries and undeveloped countries. Simulations were carried out to evaluate the performance of the developed approach. Promising results thus achieved exhibit performance better than genetic algorithm (GA)-based and Full-search algorithms in terms of decreasing the number of MSE computations. The number of MSE computations was reduced by the proposed algorithm for 463 times faster compared to the Full-search algorithm, although the retrieved image quality did not have a considerable change.

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