Global median filtering forensic method based on Pearson parameter statistics

摘要

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 ${i kappa }$kappa, ${i kappa }$kappa is defined as the polynomial ratio of skewness and kurtosis. To capture fingerprints of median filtering, ${i kappa }$kappa 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.