Improvement Feature Vector: Autoregressive Model of Median Filter Residual

Abstract

To detect median filtering forensics, a four-feature ensemble including the median filter residual autoregressive (MFR AR) model, statistical properties, gradient-edge line, and HU invariant moments of an image were used to propose an improved feature vector. The defined novel feature vector was trained on a support vector machine (SVM) classifier for median filtering detection (MFD) of forgery images. The performance of the proposed MFD scheme was measured with several types of images: median filtered (window size: {3 × 3, 5 × 5, composite (3 × 3, 5 × 5)}), JPEG compressed (quality factor: 90) after median filtered, rotated (counterclockwise: 5%), and noise added (salt-pepper: 5%) which has been re-altered in various ways. Experimental results show high efficiency and performance of the MFD techniques. The area under the curve (AUC) by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed MFD scheme are 0.9 upper with the trained SVM classifier. Thus, the grade evaluation of the proposed scheme is ``Excellent (A)”.