With billions of digital images flooding the internet which are widely used and regards as the major information source in many fields in recent years. With the high advance of technology, it may seem easy to fraud the image. In digital images, copy-move forgery is the most common image tampering, where some object(s) or region(s) duplicate in the digital image. The important research has attracted more attention in digital forensic is forgery detection and localization. Many techniques have been proposed and many papers have been published to detect image forgery. This paper introduced a review of research papers on copy-move image forgery published in reputed journals from 2017 to 2020 and focused on discussing various strategies related with fraud images to highlight on the latest tools used in the detection. This article will help the researchers to understand the current algorithms and techniques in this field and ultimately develop new and more efficient algorithms of detection copy-move image.

The technological development in the field of information and communication has been accompanied by the emergence of security challenges related to the transmission of information. Encryption is a good solution. An encryption process is one of the traditional methods to protect the plain text, by converting it into inarticulate form. Encryption implemented can be occurred by using some substitute techniques, shifting techniques, or mathematical operations. This paper proposed a method with two branches to encrypt text. The first branch is a new mathematical model to create and exchange keys, the proposed key exchange method is the development of Diffie-Hellman. It is a new mathematical operations model to exchange keys based on prime numbers and the possibility of using integer numbers. While the second branch of the proposal is the multi-key encryption algorithm. The current algorithm provides the ability to use more than two keys. Keys can be any kind of integer number (at least the last key is a prime number), not necessarily to be of the same length. The Encryption process is based on converting the text characters to suggested integer numbers, and these numbers are converted to other numbers by using a multilevel mathematical model many times (a multilevel process depending on the number of keys used), while the decryption process is a one-level process using just one key as the main key, while the other keys used as secondary keys. The messages are encoded before encryption (coded by ASCII or any suggested system). The algorithm can use an unlimited number of keys with a very large size (more than 7500 bytes), at least one of them a prime number. Exponentiation is also used for keys to increase complexity. The experiments proved the robustness of the key exchange protocol and the encryption algorithm in addition to the security. Comparing the suggested method with other methods ensures that the suggested method is more secure and flexible and easy to implement.

Color information is useless for distinguishing significant edges and features in numerous applications. In image processing, a gray image discards much-unrequired data in a color image. The primary drawback of colour-to-grey conversion is eliminating the visually significant image pixels. A current proposal is a novel approach for transforming an RGB image into a grayscale image based on singular value decomposition (SVD). A specific factor magnifies one of the color channels (Red, Green, and Blue). A vector of three values (Red, Green, Blue) of each pixel in an image is decomposed using SVD into three matrices. The norm of the diagonal matrix was determined and then divided by a specific factor to obtain the grey value of the corresponding pixel. The contribution of the proposed method gives the user high flexibility to produce many versions of gray images with varying contrasts, which is very helpful in many applications. Furthermore, SVD allows for image reconstruction by combining the weighting of each channel with the singular value matrix. This results in a grayscale image that more accurately captures the actual intensity values of the image and preserves more color information than traditional grayscale conversion methods, resulting in loss of color information. The proposed method was compared with a similar method (converting the color image into grayscale) and was found to be the most efficient.INDEX TERMS Decolorization, grey image, image conversion, SVD, technological development. I. INTRODUCTIONCurrently, most of the captured images are color images. However, it is frequently necessary to convert a color image to grayscale images for printing, aesthetic intents, object detection, and publishing as less expensive, helping colorblind people preserve visual cues [1]. Color-to-grayscale conversion is the process of reducing the image dimensions by transforming the RGB tristimulus values (Red, Green, Blue) ∈ R 3 to the intensity value (I) R [2]. The lightness valuesThe associate editor coordinating the review of this manuscript and approving it for publication was Mingbo Zhao . ranged from zero (black) to 255 (white), as shown in Fig. 1. Generally, the conversion process produces an image with lower contrast than the original color image. The goal of each conversion algorithm is to preserve the local luminance consistency, global consistency, image contrast information, and hue order as much as possible [3]. Different processing methods are required for each pixel in various image-processing applications. Processing RGB pixels is not feasible due to high storage requirements and computation costs. The best solution to these issues is to convert an RGB image into a grayscale image [4]. Grayscale images are mainly used for shape characteristics, edge detection, circular objects, and

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Alzheimer’s disease (AD) progression can be avoided by conducting diagnosis beforehand. This diagnosis acquired quick preventive care which could be possibly done by specialists. Fast and accurate evaluation at the earliest and most challenging stage were required to detect in the diagnosis of AD. In this paper, previous studies were reviewed into a better approach that recognizes the presence of disease in sagittal magnetic resonance automatically (MRI) images that are unusually used. The MRI brain images were used to identify and distinguish characteristics using a range of characteristics recognition techniques. The review of research papers on Alzheimer’s Disease published in reputable journals from 2017 to 2020 were presented and discussion of various strategies related to the latest tools used in early diagnosis is our main focus in this study, which could enable researchers to understand current algorithms and techniques in this area, and eventually develop new and more effective algorithms.