Communication systems: An introduction to signals and noise in electrical communication

The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where all meaningful regions (objects) are segmented, the localization of image manipulation focuses only the possible tampered region which makes the problem even more challenging. In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions. One of the key properties of manipulated regions is that they exhibit discriminative features in boundaries shared with neighboring non-manipulated pixels. Our motivation is to learn the boundary discrepancy, i.e., the spatial structure, between manipulated and non-manipulated regions with the combination of LSTM and convolution layers. We perform end-to-end training of the network to learn the parameters through back-propagation given ground-truth mask information. The overall framework is capable of detecting different types of image manipulations, including copy-move, removal and splicing. Our model shows promising results in localizing manipulated regions, which is demonstrated through rigorous experimentation on three diverse datasets.

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Exploiting Spatial Structure for Localizing Manipulated Image Regions

A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India

Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes.

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Intuitionistic Fuzzy Edas Method: an Application to Solid Waste Disposal Site Selection

A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures

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.

/pdf/pixel-based-image-forgery-detection-a-review-ju5vf5j3iv.pdf

Pixel-Based Image Forgery Detection: A Review

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.

On K-means data clustering algorithm with genetic algorithm

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.

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

Edge detection is one of the most fundamental operations in image processing and is one of the most commonly used operations in image processing and pattern recognition. The reason for this is that edges form the outline of an object and thus reduce the size of file without losing the useful information. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. Edge detection reduces the amount of data needed to process by removing unnecessary features. Knowing the positions of these boundaries is critical in the process of image enhancement, recognition, restoration and compression. The edges of image are considered to be most important image attributes that provide valuable information for human image perception. The areas of this work are in digital image process and telecommunication engineering, which are very wide fields. In this paper a comparison of different edge detectors has been made and results formed using the values of mean square error and peak signal to noise ratio shows that intuitionistic fuzzy edge detector outperform over the existed edge detectors.

A comparative study of edge detectors in digital image processing

The agricultural production is affected by the climate changes i.e. humidity, rain, extremes of temperature etc. Additionally, abiotic stresses are causative element to the etiology of disease as well as pest on crops. The production of the crops can be improved by diagnosis as well as detecting the accurate disease on time or in early stage. Moreover, it is very difficult for accurately detecting and treatment based on the technique which used in disease and insect pests diagnosis. Few researchers have made efforts on predicting disease as well as pest crops using machine learning algorithms. Therefore, this paper presents disease identification in corn crops by analyzing the leaves in the very early stage. We have used PlantVillage dataset for experiments and analysis. The validity of the results has been cheeked on various performance metrics such as precision, accuracy, recall, storage space, running time of the model and AUC-RoC. The obtained results shows the proposed technique outperform in comparison with the traditional machine learning algorithms. Developed model is able to achieve the accuracy of 94%.

Mohd Dilshad Ansari

Papers

Pixel-Based Image Forgery Detection: A Review

On K-means data clustering algorithm with genetic algorithm

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

A comparative study of edge detectors in digital image processing

A Convolution Neural Network based approach to detect the disease in Corn Crop