Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

作     者：S.B.G.Tilak Babu Ch Srinivasa Rao 

作者机构：University College of EngineeringJNTU KakinadaKakinada 533003India. Department of ECEJNTUK UCEVizianagaram 535003India. 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2023年第6卷第3期

页      面：347-360页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：copy move forgery detection image authentication passive image forgery detection blind forgery detection 

摘      要：Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research *** copy-move forgery,the assailant intends to hide a portion of an image by pasting other portions of the same *** detection of such manipulations in images has great demand in legal evidence,forensic investigation,and many other *** paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors,such as local ternary pattern,local phase quantization,local Gabor binary pattern histogram sequence,Weber local descriptor,and local monotonic pattern,and classifiers such as optimized support vector machine and optimized *** proposed algorithms can classify an image efficiently as either copy-move forged or authenticated,even if the test image is subjected to attacks such as JPEG compression,scaling,rotation,and brightness ***,CASIA,and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed *** proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.