Global median filtering forensic method based on Pearson parameter statistics
Abhinav Kumar Gupta, Divya Singhal · IET Image Processing · 2019
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 , is defined as the polynomial ratio of skewness and kurtosis. To capture fingerprints of median filtering, 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.