Removing the traces of median filtering via unsharp masking as an anti-forensic approach in medical imaging

Abstract

Development of post-processing algorithms which cannot be detected by forensic tools is an active area of research in image processing. Median Filter (MF) is one among the denoising schemes which is specifically targeted by the forensic toolsbecause of its wide application in commercial raster graphic editors, simplicity, fast computation and detail preserving characteristics. Methodsbased on Convolutional Neural Networks (CNN) and Variational Deconvolution (VD), meant for reducing the forensic detectability of MF by removing the traces of filtering from the output images are computationally intense. A simple and computationally feasible approach for removing the traces of median filtering from the output images, thereby to reduce the forensic detectability of MF is proposed in this paper. In the proposed approach, blurred edges in the output of MF are restored with the help of Unsharp Masking (UM). Optimum value of the amount which controls the degree of sharpening in the UM algorithm is determined via minimum error sense criterion by making use of Peak Signal to Noise Ratio (PSNR) between input and processed images as objective function. Values of PSNR and Structural Similarity Index Metric (SSIM) between input and output images exhibited by the proposed algorithm are found to be higher than those exhibited by methods based on CNN, VD and combined framework of VD and Total Variation (TV) minimisation.