Noura A. Semary

Efficient implementation of chaotic image encryption in transform domains

Investigation of transform domain encryption.IWT encryption study.Study of chaotic Baker map permutation. The primary goal of this paper is security management in data image transmission and storage. Because of the increased use of images in industrial operations, it is necessary to protect the secret data of the image against unauthorized access. In this paper, we introduce a novel approach for image encryption based on employing a cyclic shift and the 2-D chaotic Baker map in different transform domains. The Integer Wavelet Transform (IWT), the Discrete Wavelet Transform (DWT), and the Discrete Cosine Transform (DCT) are exploited in the proposed encryption approach. The characteristics of the transform domains are studied and used to carry out the chaotic encryption. A comparison study between the transform-domain encryption approaches in the presence of attacks shows the superiority of encryption in the DWT domain.

Texture Recognition Based Natural Gray Images Coloring Technique

In this paper, we propose a new technique for computer coloring gray scale images. This technique works for texture based images like natural scenes. Its based on segmenting the image into different regions according to their textures and then classifying these textures to predefined texture classes to get their real colors. Recognition of these textures is performed by matching these textures with a training set stored in a special database. Most of authors working in coloring of gray scale images have used primitive methods for coloring which are both inaccurate and limited. At the end of this paper we show that our coloring system has succeeded to colorize the natural gray images with real colors in high quality.

A proposed accelerated image copy-move forgery detection

Image forgery detection is currently one of the interested research fields of image processing. Copy-Move (CM) forgery is one of the frequently used techniques. In this paper, we propose a method which is efficient and fast for detect copy-move regions. The proposed method accelerates block matching strategy. Firstly, the image is divided into fixed-size overlapping blocks then discrete cosine transform is applied to each block to represent its features. Fast k-means clustering technique is used to cluster the blocks into different classes. Zigzag scanning is performed to reduce the length of each block feature vector. The feature vectors of each cluster blocks are lexicographically sorted by radix sort, correlation between each nearby blocks indicates their similarity. The experimental results demonstrate that the proposed method can detect the duplicated regions efficiently, and reduce processing time up to 50% of other previous works.

Copy-rotate-move forgery detection based on spatial domain

Digital image tampering becomes a common information falsification trend. Copy-Move forgery is one of the tampering types that are used. Image forgery is the science of detecting image tempering whether with a previous knowledge about the source image (active) or without (passive). In this paper, we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain. The proposed method accelerates blocking matching strategy by parallel comparing between blocks. Firstly, the image is divided into fixed-size overlapping blocks then features are extracted for each block. k-means clustering technique is used to cluster the blocks into different cluster. The feature vectors of each cluster blocks are lexicographically sorted by radix sort, and then a similarity measure is calculated between each nearby blocks to determine their similarity. The experimental results show that the proposed method can detect the duplicated regions efficiently even when an image was modified by jpeg compression, rotation and smoothing conditions. The proposed system reduced processing time up to 75% of other previous works.

A texture recognition coloring technique for natural gray images

Gray image coloring is utilized to increase the visual appeal of images such as old black and white photos, movies or scientific illustrations. Most of authors working in coloring of gray scale images have used primitive methods for coloring which are both inaccurate and limited. In this paper we propose a new technique for computer coloring gray scale images. This technique works for texture based images like natural scenes. Its based on segmenting the image into different regions according to their textures. Real colors of the image can then be obtained by classifying these textures to predefined texture classes. Recognition of these textures is performed by matching these textures with a training set stored in a special database. We validate the efficiency of our coloring system by coloring several sets of natural gray images with high quality real colors.

Robust Copy–Move forgery revealing in digital images using polar coordinate system

Abstract Copy–Move (CM) is a common type of digital image forgery where a part of the original image is copied and pasted at another position in the same image. Sometimes, to increase the invisibility of the copying process, some processing procedures on the copied region are performed before or after pasting. Although block matching (BM) is the common well-used procedure for CM tempering detection, blind CM detection with geometric or intensity transformations is still a challenge. In this paper, we propose an efficient methodology for enhancing BM based Copy–Move forgery detection. The main contribution of this work is the utilization of polar representation to get the representative features for each block. The main feature used in this paper is the frequency of each block based on Fourier transform. The experimental results show the efficiency of the proposed method for detecting CM regions, even when the copied region has undergone severe image manipulations such as rotation, scaling, Gaussian blurring, brightness modification, JPEG compression and noise addition.

A proposed framework for robust face identification system

Human face is the most representative part of body that can be used to differentiate one person among others. Accurate face identification system is still a challenge to Image Processing and Pattern Recognition researchers. In this paper, a complete framework for face-based personal identification system is proposed. The proposed frame work is composite of three basic stages; face skin detection (FSD), facial features positioning (FFP), representative features extraction (RFE) and face matching (FM). For FSD stage, RGB-H-CbCr color model is used after a comparative study between different color models. Enhanced Haar-like features are utilized for FFP stage. After accurate features positioning, the representative features are calculated using the centers of eyes, nose and mouth organs. The experimental results of this paper depict that the proposed frame work accurately identify persons of The Center for Vital Longevity Face Database. The proposed system could Identify the correct person with 40 saved image with accuracy 98%, while it could reject wrong persons with accuracy 98.17%. The overall accuracy of correct identification reaches 98.14%.

Fan Search for Image Copy-Move Forgery Detection

Image forgery detection is currently one of the interested research fields of image processing. Copy-Move (CM) forgery is one of the most commonly techniques. In this paper, we propose an efficient methodology for fast CM forgery detection. The proposed method accelerates blocking matching strategy. Firstly, the image is divided into fixed-size overlapping blocks then Discrete Cosine Transform (DCT) is applied to each block to represent its features, which are used to indirectly compare the blocks. After sorting the blocks based on DCT coefficients, a distance is measured between nearby blocks to denote their similarity. The proposed Fan Search (FS) algorithm starts once a duplicated block is detected. Instead of exhaustive search for all blocks, the nearby blocks of the detected block are examined first in a spiral order. The experimental results demonstrate that the proposed method can detect the duplicated regions efficiently, and reduce processing time up to 75% less than other previous works.

Hybrid encoding scheme for HSI model using the Minimum Color Difference

Its known that nonlinear color models like Hue-Saturation -Value/ Brightness/ Luminance/ Intensity (HSV/ HSB/ HSL/ HSI) have special feature for each channel. So in this paper we propose a hybrid compression system that deals with each channel with a suitable compression technique to obtain encoded images with less size and high decoding quality than the traditional encoding methods. There are three encoding techniques will be mixed in our proposed system; Object Compression Technique for the Hue channel, Minimum Color Difference for Saturation, and the standard Jpeg2000 encoding technique for the Intensity channel. The proposed system results in high compression ratio with very good decoding quality.

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

Content-based Image Retrieval (CBIR) is retrieving the desired images from huge collections. The user queries are becoming very specific and traditional text-based methods cannot efficiently handle them. CBIR system retrieves the image via low-level features such as color, texture and shape. In this work, we propose CBIR system that retrieves images from a database based on the semantic features of them. Our methodology divide the query image into 100 regions. And then, extracts Features Vector from each region and label each one with the suitable concept like (Sky, Sand, Water, trunks, foliage, rocks,..., and Grass). The labeling process in performed semi-automatically using k-means clustering and KNN classification algorithms. The system has been evaluated by recall and precision measures and compared to other recent works. The results of the paper reflects the efficiency of the system for retrieving images with up to 98% recognition ratio. General Terms Image processing, Image representations, Visual contentbased indexing and retrieval.