Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

作     者：C.D.Prem Kumar S.Saravana Sundaram 

作者机构：Department of Information TechnologyHindusthan College of Engineering and TechnologyCoimbatore641032TamilnaduIndia Department of Electrical and Electronics EngineeringHindusthan College of Engineering and TechnologyCoimbatore641032TamilnaduIndia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第1期

页      面：881-899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）] 

主　　题：Copy move detection image forgery deep learning machine learning parameter tuning forensics 

摘      要：The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image *** effective technique for tampering the identification is the copy-move *** image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data ***-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical *** this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)*** presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved *** perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful ***,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged *** utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification *** ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 *** experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.