Title: Passive contrast enhancement detection using NSCT-based statistical features and ensemble classifier

Authors: Gajanan K. Birajdar; Vijay H. Mankar

Addresses: Department of Electronics Engineering, Ramrao Adik Institute of Technology, DY Patil University, Navi Mumbai, Maharashtra, 400706, India ' Department of Electronics and Telecommunication Engineering, Government Polytechnic, Nagpur, Maharashtra, 440001, India

Abstract: Due to widespread use of digital images and sophisticated image editing software, it is quite easy to create digital image forgeries without leaving any visual traces of doctoring. Contrast enhancement (CE) processing is popularly used to hide the traces of doctoring in copy-and-move image forgery operation by malicious users. In this paper, global blind contrast enhancement detection algorithm is proposed using various statistical parameters based on Gaussian distribution and generalised Gaussian distribution features, energy and grey level run length matrix (GLRLM) descriptors after NSCT decomposition. Fisher feature selection criterion is utilised to choose the most relevant features and to remove the less important features. Detection accuracy of the algorithm is investigated using various ensemble classifiers architectures. Experimental results are presented using four different ensemble classifier architectures class-I to class-IV for C_b and grey image database. The proposed algorithm outperforms all the existing feature-based approaches compared using the detection accuracy.

Keywords: blind image forgery detection; non-subsampled contourlet transform; grey level run length matrix; GLRLM; generalised Gaussian distribution; classifier ensemble.

DOI: 10.1504/IJESDF.2022.123855

International Journal of Electronic Security and Digital Forensics, 2022 Vol.14 No.4, pp.341 - 372

Received: 27 Mar 2021
Accepted: 30 Jun 2021
Published online: 04 Jul 2022 *

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