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

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

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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.

copy move forgery detectionimage authenticationpassive image forgery detectionblind forgery detection

S.B.G.Tilak Babu、Ch Srinivasa Rao

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University College of Engineering,JNTU Kakinada,Kakinada 533003,India

Department of ECE,JNTUK UCE,Vizianagaram 535003,India

2023

大数据挖掘与分析(英文版)

大数据挖掘与分析(英文版)

CSCDEI
ISSN:
年,卷(期):2023.6(3)
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