Hybrid deep learning and machine learning approach for passive image forensic

被引：7

作者：

Thakur, Abhishek ^{[1
]}

Jindal, Neeru ^{[1
]}

机构：

[1] Thapar Inst Engn & Technol, Elect & Commun Engn Dept, Patiala, Punjab, India

来源：

IET IMAGE PROCESSING | 2020年 / 14卷 / 10期

关键词：

Gabor filters; image classification; iris recognition; feature extraction; learning (artificial intelligence); neural nets; image forensics; image segmentation; image coding; machine learning-based approach; passive image forgery detection; DL algorithm; forged forged categories; not forged categories; CMFD image manipulation dataset; machine learning approach; passive image forensic; forgeries; emerging methods; deep neural network algorithm; SEGMENTATION; NETWORKS;

D O I：

10.1049/iet-ipr.2019.1291

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.

引用

页码：1952 / 1959

页数：8

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