NDLI: Global median filtering forensic method based on Pearson parameter statistics

Content Provider	IET Digital Library
Author	Gupta, Abhinav Singhal, Divya
Abstract	Median filtering forensics in images is a subject under intense study nowadays. Existing median filtering detectors are developed based on hand-crafted features and convolutional neural networks (CNN). Among hand-crafted features based detectors, most of the detector's performance deteriorate for low-resolution images compressed with low-quality factors. However, CNN-based detectors are found to be more robust at the expense of large database and large training time requirement. In this study, the authors propose a robust median filtering detector by exploiting the statistics of the Pearson parameter κ , κ is defined as the polynomial ratio of skewness and kurtosis. To capture fingerprints of median filtering, κ is determined for the median filtered residual (MFR) of the images to construct a novel feature set of 23 dimensions. The efficacy of the proposed feature set, against existing hand-crafted features based and CNN-based detectors, is established by a series of experiments for global median filtering detection. Results reveal that the proposed feature set exhibits performance gain of 2–4% against existing hand-crafted features based detectors and an approximate gain of 4% against CNN-based detector for detection of low-resolution median filtered images compressed with low-quality factors.
Starting Page	2045
Ending Page	2057
Page Count	13
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6074
Journal	IET Image Processing
Publisher Date	2019-03-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CNN-based Detector Computer Vision And Image Processing Technique Convolutional Neural Network Data Compression Feature Extraction Filtering Method in Signal Processing Global Median Filtering Forensic Method Global MFD Hand-crafted Feature Extraction Image And Video Coding Image Coding Image Filtering Image Recognition Image Representation Image Resolution Low-quality Factors Low-resolution Image Compression Low-resolution Median Median Filter Median Filtered Residual Median Filtering Detection Median Filtering Detector Passive Image Forensics Pearson Parameter Statistics Performance Gain Polynomial Ratio Robust Median Filtering Detector
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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