Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach

Recently, the median filtering (MF) detector as a forensic tool for the recovery of images' processing history has attracted wide interest. This paper presents a novel method for the blind detection of MF in digital images. Following some strongly indicative analyses in the difference domain of images, we introduce two new feature sets that allow us to distinguish a median-filtered image from an untouched image or average-filtered one. The effectiveness of the proposed features is verified with evidence from exhaustive experiments on a large composite image database. Compared with prior arts, the proposed method achieves significant performance improvement in the case of low resolution and strong JPEG post-compression. In addition, it is demonstrated that our method is more robust against additive noise than other existing MF detectors. With analyses and extensive experimental researches presented in this paper, we hope that the proposed method will add a new tool to the arsenal of forensic analysts.