The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

Praise be to Allah , the Lord of Heavens , Who revealed His Scripture in plain Arabic, and prayers and peace be upon the Master of all creatures and the chosen one , Muhammad (PBUH).Pushkin was known as a poet in Russia, the sun of its poetry that set, the father of the Russian literature and the founder of its literary language. When he published his poems, trying to express his inner poetic feelings and creative visions, he presented them with creativity and special touch. The reason behind the selection of Pushkin as the subject of this study is that he was affected by Islam and Arabs. For the purpose of this study, the dissertation has been divided into introduction and three chapters; each chapter includes two topics. In the introdu

The study explains how translation participates in shaping the responses of different communities to global awareness campaigns, and the extent to which language and translated terminology from English to Arabic influence the audience’s understanding and interaction with the awareness messages delivered by these campaigns. The research aims to explore the problems in translating these campaign titles, which may lead to vague meaning, as the process is considered a major challenge due to the need to navigate linguistic nuances, cultural differences, and contextual variations. In this study, some awareness phrases were randomly chosen from many types of organizations’ campaigns, like the WHO, UN, mental health, and Empowerment, a

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

The absurdity of Orientalist thought and its deviation in interpretation
Quranic text
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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d