In the last few years, due to the continuous advancement of technology, human behavior detection and recognition have become important scientific research in the field of computer vision (CV). However, one of the most challenging problems in CV is anomaly detection (AD) because of the complex environment and the difficulty in extracting a particular feature that correlates with a particular event. As the number of cameras monitoring a given area increases, it will become vital to have systems capable of learning from the vast amounts of available data to identify any potential suspicious behavior. Then, the introduction of deep learning (DL) has brought new development directions for AD. In particular, DL models such as convolution neural networks (CNNs) and recurrent neural networks (RNNs) have achieved excellent performance dealing with AD tasks, as well as other challenging domains like image classification, object detection, and speech processing. In this review, we aim to present a comprehensive overview of those research methods using DL to address the AD problem. Firstly, different classifications of anomalies are introduced, and then the DL methods and architectures used for video AD are discussed and analyzed, respectively. The revised contributions have been categorized by the network type, architecture model, datasets, and performance metrics that are used to evaluate these methodologies. Moreover, several applications of video AD have been discussed. Finally, we outlined the challenges and future directions for further research in the field.

Information safety has become a vital issue and data encryption and decryption have lately been vastly researched and developed. There is a need for a powerful encryption and decryption which is very tough to crack, so cryptography plays a major role to fulfill these demands. Moreover, parallel algorithm is rapidly becoming cost and resource-efficient as a result of a unified approach to solve computer problems and wide-ranging problems encountered in applied sciences and engineering applications. In this paper, a parallel environment has been utilized to construct a new encryption system, based on involving the so-called 'zig-zag' ordering that is used in JPEG data compression. A new chaotic system of three dimensions will be produced to eliminate the common encryption systems problems.

This paper aims to present a parallel implementation based color image encryption using non -linear chaotic system. The adopted chaotic system was suggested and approved in our previous work [1] which generates key streams with chaotic behavior. In this paper, pixel level permutation algorithm based on chaotic map generation is investigated and analyzed. The encryption–decryption schemes are achieved in parallel and composed of three main phases: chaotic keys generation, pixel-level permutation and bit-level diffusion phase. Both permutation and diffusion processes are achieved according to the chaotic keys. The parallel implementation of the proposed image encryption system is realized and inspired with parallel computing library offered by Matlab 2018, which equips highly performance than the pipeline ones and would be helpful to utilize in image encryption/decryption for real time application. Security and statistical analysis in addition to the main differential attacks analysis are specified to evaluate the performance of the proposed image encryption algorithm with parallel implementation. From the experimental results, the output image of the encryption task shows a higher randomness of the encrypted image which can be effectively resistant to attacker. Furthermore, the run time of encryption process is faster than other research works.

One of the most difficult issues in the history of communication technology is the transmission of secure images. On the internet, photos are used and shared by millions of individuals for both private and business reasons. Utilizing encryption methods to change the original image into an unintelligible or scrambled version is one way to achieve safe image transfer over the network. Cryptographic approaches based on chaotic logistic theory provide several new and promising options for developing secure Image encryption methods. The main aim of this paper is to build a secure system for encrypting gray and color images. The proposed system consists of two stages, the first stage is the encryption process, in which the keys are generated depending on the chaotic logistic with the image density to encrypt the gray and color images, and the second stage is the decryption, which is the opposite of the encryption process to obtain the original image. The proposed method has been tested on two standard gray and color images publicly available. The test results indicate to the highest value of peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), number of pixel change rate (NPCR) are 7.7268, 50.2011 and 100, respectively. While the encryption and decryption speed up to 0.6319 and 0.5305 second respectively.

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