The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI genera

In this study, the response and behavior of machine foundations resting on dry and saturated sand was investigated experimentally. In order to investigate the response of soil and footing to steady state dynamic loading, a physical model was manufactured. The manufactured physical model could be used to simulate steady state harmonic load at different operating frequencies. Total of (84) physical models were performed. The parameters that were taken into considerations include loading frequency, size of footing and different soil conditions. The footing parameters were related to the size of the rectangular footing and depth of embedment. Two sizes of rectangular steel model footing were used (100 200 12.5 mm) and (200 400 5.0 mm).

The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,