Median filter detection through streak area analysis

Abstract

Median filter (MF) is a content preserving nonlinear filter, employed to hide traces of image manipulations, affecting the reliability of manipulation detection techniques. Thus, median filter detection is a major concern for digital image forensics (DIF) experts. The methods used for median filter detection (MFD) are computationally expensive as high dimensionality of feature vectors are employed. This paper proposes an effective method for blind median filter detection based on streaking effect of the median filter. The method offered in the paper is built on experimental observation that the percentage streak area (psa) of an image increases on repetitive median filtering of the image, the rate at which psa increases for median filtered images is different from the rate at which psa increases for unfiltered images. A feature vector based on the observation is extracted from three different image datasets UCID, BOSS and Dresden and feed to Support Vector Machine (SVM) to perform 10-fold cross validation using linear kernel. The results obtained, using a three-dimensional feature vector, demonstrates efficacy of the proposed method.