<span>Face detection is a critical function of security (secure witness face in the video) who appear in a scene and are frequently captured by the camera. Recognition of people from their faces in images has recently piqued the scientific community, partly due to application concerns, but also for the difficulty this characterizes for the algorithms of artificial vision. The idea for this research stems from a broad interest in courtroom witness face detection. The goal of this work is to detect and track the face of a witness in court. In this work, a Viola-Jones method is used to extract human faces and then a particular transformation is applied to crop the image. Witness and non-witness images are classified using convolutional neural networks (CNN). The Kanade-Lucas-Tomasi (KLT) algorithm was utilized to track the witness face using trained features. In this model, the two methods were combined in one model to take the advantage of each method in terms of speed and reduce the amount of space required to implement CNN and detection accuracy. After the test, the results of the proposed model showed that it was 99.5% percent accurate when executed in real-time and with adequate lighting.</span>

The encryption of digital images plays a very important role in maintaining privacy and security, in order to protect the people, like witnesses. So, an individual image must be encrypted before it is sent over the Internet to the intended recipient. In this paper, images are encrypted using the rabbit algorithm and the chaotic equation represented by Lévy flight for several reasons, including employing beginning seed number, variance of parameters, and unpredictable random walk direction by making the encryption key more robust through the randomization of the initial vector values. After applying the original and improved rabbit encryption algorithms to the images, the results showed that the proposed Rabbit cipher has shown higher robustness against brute force assaults and larger capability of defending against the statistical cracking. Which has been verified and validated through the use of security metrics are PSNR, MSE, and entropy criteria., The encryption of a 256-size image was achieved in just 3 seconds when the improved Rabbit algorithm has been used, so it will be possible to encrypt video frames in real time because it requires a lesser time and having higher security.

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In this research, the well-known encryption algorithms in the encryption Systems, namely DES & RC4 and the advantages and disadvantages of each algorithm are reviewed and evaluated. These two algorithms are combined to produce of new algorithm which is more efficient unscrambling due to the increasing of the level of complexity that make it highly resistance to several attacks. The new algorithm is implemented to show its efficiency in term of time complexity, (i.e. Breaking the code will be much more complicated than if it would have been occurring through the use of each algorithm individually), this process can be achieved with a very small time difference (approximately neglected in the encryption process(. When this algorithm is applied and tested in practice the following result has been obtained: When a block is encrypted using the DES algorithm, the time spent may be (0.000034) milliseconds, but when using the new algorithm to encrypt the same block, the time taken will be about (0.000042) milliseconds. To encrypt 1024 blocks using the DES algorithm, it will take a time of (0.0406),while by using the new algorithm the time taken for encryption is only (0.051). This gives very little increase in time compared to increasing of complexity obtained. Since the new algorithm combining from the two previous, ones allows us to encrypt each block with a key differs from the other one (i.e. each block is encrypted with a different key depending on the preceding block), making it very difficult to break the code leading to an increase in security and information protection against decoding.

Over the previous three decades, the area of computer networks has progressed significantly, from traditional static networks to dynamically designed architecture. The primary purpose of software-defined networking (SDN) is to create an open, programmable network. Conventional network devices, such as routers and switches, may make routing decisions and forward packets; however, SDN divides these components into the Data plane and the Control plane by splitting distinct features away. As a result, switches can only forward packets and cannot make routing decisions; the controller makes routing decisions. OpenFlow is the communication interface between the switches and the controller. It is a protocol that allows the controller to identify the network packet’s path across the switches. This project uses the SDN environment to implement the firefly optimization algorithm to determine the shortest path between two nodes in a network. The firefly optimization algorithm was implemented using Ryu control. The results reveal that using the firefly optimization algorithm improves the selected short path between the source and destination.