Mohd Dilshad Ansari

Pixel-Based Image Forgery Detection: A Review

ABSTRACT With the advancement of the digital image processing software and editing tools, a digital image can be easily manipulated. The detection of image manipulation is very important because an image can be used as legal evidence, in forensics investigations, and in many other fields. The pixel-based image forgery detection aims to verify the authenticity of digital images without any prior knowledge of the original image. There are many ways for tampering an image such as splicing or copy-move, resampling an image (resize, rotate, stretch), addition and removal of any object from the image. In this paper we have discussed various pixel-based techniques for image forgery detection, mainly copy-move and splicing techniques.

New Divergence and Entropy Measures for Intuitionistic Fuzzy Sets on Edge Detection

Edges of the image play an important role in the field of digital image processing and computer vision. The edges reduce the amount of data, extract useful information from the image and preserve significant structural properties of an input image. Further, these edges can be used for object and facial expression detection. In this paper, we will propose new intuitionistic fuzzy divergence and entropy measures with its proof of validity for intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. To check the robustness of the proposed method, obtained results are compared with Canny, Sobel and Chaira methods. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) have been calculated and PSNR values of proposed method are always equal or greater than the PSNR values of existing methods. The detected edges of the various sample images are found to be true, smooth and sharpen.

Texture Feature Extraction Using Intuitionistic Fuzzy Local Binary Pattern

Abstract In this paper, intuitionistic fuzzy local binary for texture feature extraction (IFLBP) has been proposed to encode local texture from the input image. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy sets in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contributes to more than one bin in the distribution of IFLBP values, which can further be used as a feature vector in the various fields of image processing. The performance of the proposed method has been demonstrated on various medical images and processing images of size 256×256. The obtained results validated the effectiveness and usefulness of our proposed method over the other reported methods, and new improvements are suggested.

A New Buyer-Seller Watermarking Protocol with Discrete Cosine Transform

Digital water marking is a promising technology to embed information as unperceivable signals in digital contents. Buyer-seller watermarking protocols with Discrete Cosine Transform (DCT) integrate multimedia watermarking and fingerprinting with cryptography, for copyright protection, piracy tracing, and privacy protection. It uses the public key encryption schemes. In this paper we propose a New Buyer Seller water marking protocol with DCT which is secure and flexible and gives more security from previous watermarking protocols to both buyer and seller. In this we use Public Key Infrastructure (PKI), arbitrator and watermarking certificate authority (WCA) for better security.

On K-means data clustering algorithm with genetic algorithm

Clustering has been used in various disciplines like software engineering, statistics, data mining, image analysis, machine learning, web cluster engines, and text mining in order to deduce the groups in large volume of data. The notion behind clustering is to ascribe the objects to clusters in such a way that objects in one cluster are more homogeneous to other clusters. There are variegated clustering algorithms available viz k-means clustering, cobweb clustering, db-scan clustering, fartherstfirst clustering, and x-means clustering algorithm but K-means on the whole comprehensively used algorithm for unsupervised clustering dilemma. In this paper k-means clustering is being optimised using genetic algorithm so that the problems of k-means can be overridden. The outcomes of k-means clustering and genetic k-means clustering are evaluated and compared; obtained result shows K-means with GA algorithm suggest new improvements in this research domain.

Feature extraction method for digital images based on intuitionistic fuzzy local binary pattern

Feature extraction is an important step in the field of digital image processing, which also helps in reducing the dimensions from large data. Researchers are investigating an efficient methods as there are lot of challenges in extracting significant features from an image that can reveal essential information. However, a very small work has been reported to this research domain in the last decades. In this paper, we propose intuitionistic fuzzy local binary (IFLBP) for extracting texture feature from the input image. The proposed technique extends fuzzy local binary pattern method by including intuitionistic fuzzy set theory in the demonstration of local patterns of texture in images. The proposed algorithm has been applied on various images and obtained result shows the effectiveness of our proposed technique.

On edge detection based on new intuitionistic fuzzy divergence and entropy measures

Edges of the image plays an important role in the field of digital image processing and computer vision. It reduces the amount of data, extract useful information from the image and also preserve significant structural properties of an input image. Further, it can be used in object and facial expression detection. In this paper, we have proposed new intuitionistic fuzzy divergence and entropy measures with its proof of validity for an intuitionistic fuzzy sets. A new and significant technique has been developed for edge detection. The proposed method has been demonstrated on various sample images. The detected edges of the sample images are true, smooth and sharpen which is found to be better than existing methods.