Data hiding and watermarking are considered one of the most important topics in cyber security. This article proposes an optimized method for embedding a watermark image in a cover medium (color image). First, the color of the image is separated into three components (RGB). Consequently, the discrete wavelet transform is applied to each component to obtain four bands (high-high, high-low, low-high, and low-low), resulting in 12 bands in total. By omitting the low-low band from each component, a new square matrix is formed from the rest bands to be used for the hiding process after adding keys to it. These keys are generated using a hybrid approach, combining two chaotic functions, namely Gaussian and exponential maps. The embedding matrix is divided into square blocks with a specific length, each of which is converted using Hessenberg transform into two matrices, P and H. For each block, a certain location within the H-matrix is used for embedding a secret value; the updated blocks are assembled, and the reverse process is performed. An optimization method is applied, through the application of the firework algorithm, on the set of the initial values that generate keys. Using an optimization procedure to obtain keys requires performing lowest possible change rate in an image and maintain the quality of the image. To analyze and test the efficiency of the proposed method, mean-square error (MSE) and peak signal-to-noise ratio (PSNR) measurements are calculated. Furthermore, the robustness of the watermark is computed by applying several attacks. The experimental results show that the value of the MSE is reduced by about 0.01 while the value of the PSNR is increased by about 1.25 on average. Moreover, the proposed method achieved a high-retrieval rate in comparison with the non-optimization approach.

After the spread of autonomous driving systems in cars, the next step is the development of autonomous drone systems. This will have an application in many military and civil fields, including surveillance and photography instead of satellites, as well as in search and rescue, dissemination, aid and distribution of goods, as it can cover large areas and save effort and human cost. The goal of this paper is to discuss using autonomous drones to design a border surveillance system. Where the current surveillance systems were studied to discuss the possibility of using drones as an alternative to long-range cameras, and a design proposal was presented that works on three different levels of surveillance systems. The possibility of controlling the drone through a single-board computer that is installed on the control tower with the base of the charging station for the drone was also discussed. In order to achieve the required balance between the speed of controlling the drone and the accuracy of object detection and classification, the concept of parallel processing was applied with the fast algorithms for tracking and detection (contour detection) and the high accuracy deep learning model (YOLOv8n) for detection and classification , which was trained on a customized dataset to improve the accuracy of object detection and classification in images taken from different view angles of the drone. After testing the experimental model of the system, the results showed a good enhancement in the quality of the captured images, as well as a solution to the problem of occlusion or blurring of objects in the image. Also, the results confirmed the possibility of relying on a single-board computer in processing images and controlling the drone to track moving targets in a way that was not possible by relying on transferring images to remote central processing.

Recently, there has been a notable increase in the need for the generation, dissemination, and storage of extensive volumes of multimedia data, especially digital images, generated by different intelligent devices and sensors. Along with other security vulnerabilities, such activity results in unauthorized access and false use of data. Embedding a watermark design into a digital cover and then removing it helps to address ownership conflicts and copyright infringement concerns related to the media data. Deep-learning methods are currently rather helpful for watermarking because of their high accuracy, great precision, and strong learning ability. This article presents a comprehensive examination of watermarking techniques used in deep learning contexts. We start by explaining the basic ideas of both traditional and learning-based digital watermarking, and then we look at common watermarking methods that use deep learning models. We then provide a succinct summary and comparison of the most recent contributions in the literature. Finally, we highlight the challenges related to obfuscation and suggest avenues for further study.

Generally, the process of detecting micro expressions takes significant importance because all these expressions reflect the hidden emotions even when the person tried to conceal them. In this paper, a new approach has been proposed to estimate the percentage of sarcasm based on the detected degree of happiness of facial expression using fuzzy inference system. Five regions in a face (rightleft brows, rightleft eyes, and mouth) are considered to determine some active distances from the detected outline points of these regions. The membership functions in the proposed fuzzy inference system are used as a first step to determine the degree of happiness expression based mainly on the computed distances and then another membership function is used to estimate the percentage of sarcasm according the outcomes of the membership functions in the first step. The proposed approach is validated using some face images which are collected from the SMIC, SAMM, and CAS(ME)2 standard datasets.

A Stream Cipher (SC) is a symmetric-key encryption type that scrambles each piece of data in clear text to conceal it from hackers. Despite its advantages, it has a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this paper, we propose a graphical Domain-Specific Modeling Language (DSML) to make it easier for non-technical users and domain specialists to implement an SC domain. The proposed language, SCLang, offers great expressiveness and flexibility. Six different methods of keystream generation are provided to obtain a random sequence. In addition, fifteen tests in the NIST suite are provided for random statistical analysis. The concepts of the SC domain and their relationships are presented in a meta-model. The evaluation of SCLang is based on qualitative analysis and is presented to demonstrate its effectiveness and efficiency.