In this study, two techniques are introduced for image steganography in the spatial domain. These systems employ chaos theory to track the addresses of shuffled bits in steganography. The first system is based on the well-known LSB technique, while the second system is based on a recent approach that searches for the identical bits between the secret message and the cover image. A modified logistic map is employed in the chaotic map to generate integer chaotic series to extract the shuffled addresses bits. Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), histogram analysis and correlative analysis are used for testing and evaluating the new levels of security for the proposed techniques. The results show that the proposed methods outperform existing systems.

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Coronavirus is considered the first virus to sweep the world in the twenty-first century, it appeared by the end of 2019. It started in the Chinese city of Wuhan and began to spread in different regions around the world too quickly and uncontrollable due to the lack of medical examinations and their inefficiency. So, the process of detecting the disease needs an accurate and quickly detection techniques and tools. The X-Ray images are good and quick in diagnosing the disease, but an automatic and accurate diagnosis is needed. Therefore, this paper presents an automated methodology based on deep learning in diagnosing COVID-19. In this paper, the proposed system is using a convolutional neural network, which is considered one of the mostly prominent techniques used today for its reliability and ability to generate rapid results. The system was trained on a set of X-Ray images taken of the chest area of infected and uninfected people. The CNN structure gave accuracy, Precision, Recall and F-Measure 98%. This model is characterized by its ability to distinguish efficiently and adapt to different cases.

Bézier curve of the first rank is a simple equation in terms of form, but it is characterized by the nature of private transactions making it difficult to use in image encryption because the dispersion of color values is not enough, this results in an encrypted image that gives clear references to the original image. This weakness in the equation does not exist in the case of text encryption where enough to change the numerical values of the components of the text to get a digital matrix representing the encrypted text.Through this algorithm we have used the Bézier curve technique from the first order of image coding we used a new method to generate the coefficients of the equation where we simulated the Bazier equation where it became as follows: • y=x_1*(t-1)+x_2*t • Where 0<t<1 To illustrate the work of this technology in image encoding the core of our work is in choosing a vector (1 × 4) with four numerical components )k_1, k_2, k_3, k_4 ( So that k_1<k_2 and k_3 <k_4, k_1 and k_2 have the same signal, as well as k_3 and k_4 also have the same reference to give them t_1 and t_2 Where t_1=k_1/k_2 and t_2=k_3/k_4 which will ensure that both have t_1 and t_2 have positive values less than 1. We have thus designed the equation of Bézier curve suitable for a scattering of color values of the image process, as well we’ll see it by explaining the way in detail below and tables of readings and global standards that have been inferred by the application of the algorithm