Hybrid deep learning and machine learning approach for passive image forensic. Issue 10 (6th July 2020)

• Record Type: Journal Article
• Authors: Thakur, Abhishek; Jindal, Neeru
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 1952
• Page End: 1959
• Publication Date: 2020-07-06
• Subjects: 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; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1291
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.