SPAM revisited for median filtering detection using higher‐order difference

Abstract

AbstractIn this paper, we propose a robust median filtering detection scheme utilizing the conditional joint distribution of high‐order differences. The higher‐order differences provide additional information about the correlation between a pixel and its neighbors. The relationship between a pixel and its neighbors becomes more prominent in median‐filtered images as compared with non‐median filtered images. We evaluate the performance of our method and compare it with the state of the art median filtering detection techniques on multiple databases in various environments. We use a linear discriminant classifier based on Moore–Penrose pseudoinverse matrix for classifying the median and non‐median filtered images. Experimentally, we find that our method is more effective for all types of images including uncompressed and highly compressed JPEG low‐resolution images. We also evaluate the performance of our method on Gaussian‐filtered, averaging‐filtered, and median‐filtered images of the BOWS2 and UCID databases. Copyright © 2016 John Wiley & Sons, Ltd.