Image Manipulation Detection Through Laterally Linked Pixels and Kernel Algorithms

作     者：K.K.Thyagharajan G.Nirmala 

作者机构：RMD Engineering CollegeKavaraipettaiChennaiIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第41卷第4期

页      面：357-371页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors are sincerely grateful to the anonymous referees and the editor for their timely effort in providing constructive and value comments and suggestions that have led to a substantial improvement in the paper 

主　　题：Machine learning copy move forgery support vectors kernel feature extraction 

摘      要：In this paper,copy-move forgery in image is detected for single image with multiple manipulations such as blurring,noise addition,gray scale conver-sion,brightness modifications,rotation,Hu adjustment,color adjustment,contrast changes and JPEG ***,traditional algorithms detect only copy-move attacks in image and never for different manipulation in single *** proposed LLP(Laterally linked pixel)algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detec-tion of copied region and kernel tricks is applied for detection of multiple manip-ulations in single forged *** algorithm consists of two channels such as feeding component(F-Channel)and linking component(L channel)for linking *** algorithm linking pixels detects image with multiple manipulation and copy-move forgery due to one-to-one correspondence between pixel and neu-ron,where each pixel’s intensity is taken as input for F channel of neuron and connected for forgery identifi***,neuron is connected with neighboringfield of neuron by L channel for detecting forged images with multi-ple manipulations in the image along with copy-move,through kernel trick clas-sifier(KTC).From experimental results,proposed LLP algorithm performs better than traditional algorithms for multiple manipulated copy and paste *** accuracy obtained through LLP algorithm is about 90%and further forgery detec-tion is improved based on optimized kernel selections in classification algorithm.