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

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

机构地区：[1]University College of Engineering,JNTU Kakinada,Kakinada 533003,India. [2]Department of ECE,JNTUK UCE,Vizianagaram 535003,India.

出　　处：《Big Data Mining and Analytics》2023年第3期347-360,共14页大数据挖掘与分析(英文)

摘　　要：Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus.In copy-move forgery,the assailant intends to hide a portion of an image by pasting other portions of the same image.The detection of such manipulations in images has great demand in legal evidence,forensic investigation,and many other fields.The 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 NBC.The 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 variation.CoMoFoD,CASIA,and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms.The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

关 键 词：copy move forgery detection image authentication passive image forgery detection blind forgery detection 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]