Symmetric- Based Steganography Technique Using Spiral-Searching Method for HSV Color Images

إخفاء المعلومات يعني أخفاء المعلومات السرية في بعض الوسائط المختارة الأخرى دون ترك أي دليل واضح على تغيير ميزات الوسط الناقل. تخفي معظم طرق الاختباء التقليدية الرسالة مباشرةً في الوسائط الناقلة مثل (النص والصورة والصوت والفيديو). يترك بعض الاخفاء تأثيرًا سلبيًا على صورة الغلاف الناقلة، هذا التأثير السلبي يًمكن من اكتشاف التغير في والوسط الناقل من خلال الإنسان والآلة. الغرض من طريقة إخفاء المعلومات المقترحة هو ان جعل هذا التغيير غير قابل للكشف، يركز البحث الحالي على استخدام طريقة معقدة لمنع الكشف عن إخفاء المعلومات بواسطة الإنسان والآلة باعتماد على طريقة البحث اللولبي، تم استخدام مقاييس مؤشر التشابه الهيكلي للقياس للحصول على دقة وجودة الصورة المستردة وتم تحسين جودتها المدركة. تم حساب قيم مقاييس المعلومات من خلال التجارب العملية (الإدراك، المتانة، السعة) باستخدام تقنية الاستيفاء ومقاييس التشابه الهيكلي. تظهر النتائج التجريبية أن استخدام هذه المقاييس (PSNR و MSE و SSIM) قد حسن جودة الصورة بنسبة 87٪ وأنتج قيم PSNR (38-41 ديسيبل) و MSE = 0.6537 و SSIM = 0.8255. توضح النتائج أيضًا تقدمًا ملحوظًا في مجال إخفاء المعلومات وتزايد صعوبة اكتشافها من قِبل البشر والآلات.Steganography is defined as hiding confidential information in some other chosen media without leaving any clear evidence of changing the media's features. Most traditional hiding methods hide the message directly in the covered media like (text, image, audio, and video). Some hiding techniques leave a negative effect on the cover image, so sometimes the change in the carrier medium can be detected by human and machine. The purpose of suggesting hiding information is to make this change undetectable. The current research focuses on using complex method to prevent the detection of hiding information by human and machine based on spiral search method, the Structural Similarity Index Metrics measures are used to get the accuracy and quality of the retrieved image and to improve its perceived quality. The values of information measures are calculated through practical experiments of (perceptibility, robustness, capacity) by using interpolation technique and structural similarity measures. Experimental results show that the use of these measures (PSNR, MSE, and SSIM) has improved the image quality by 87% and has produced values of PSNR (38-41 dB), MSE = 0.6537 and SSIM= 0.8255. The results also demonstrate a remarkable progress in the field of hiding information and the increasing difficulty of detecting it by humans and machines

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%

Identification Based on Iris Detection Technique

Iris-biometrics are an alternative way of authenticating and identifying a person because biometric identifiers are unique to people. This paper introduces a method aims to efficient human identification by enhanced iris detection method within acceptable time. After preparing various type of images, then perform a series of pre-processing steps and standardize them, after that use Uni-Net learning, so identify the human by Navie-Bays method is the last step based on the output of Uni-Net which is role as feature extractor for the iris part and another sub-net for non-iris part that may involve identification-outcome. The outcome of this method looked good compared to some high-level methods, so, was accuracy-rate 9855, 99.25, and 99.81 for CASIA-v4, ITT-Delhi, and MMU-database respectively. Also, this paper introduces a method of iris recognition using CNN model which is improved the preprocessed patterns that together from dataset applied some procedures to develop them based on techniques of equalization and acclimate contrast ones. After that characteristic extracted and classified using CNN that comprises of 10 layers with back-propagation schema and adjusted moment evaluation Adam-optimizer for modernize weights. The overall accuracy was 95.31% with utilization time 17.58 (mints) for training-model