A Robust Residual Dense Neural Network For Countering Antiforensic Attack on Median Filtered Images

Abstract

Recently, antiforensic methods have been proposed that invalidate most of the state-of-the-art median filter digital image forensic techniques. Also, the existing counter antiforensic methods decline noticeably when evaluated on small-sized patches in JPEG compressed images. In this letter, we have developed a robust residual dense (Neural) network-based counter antiforensic median filter detection technique that exploits local dense connection and residual learning of features for improved classification of images. Experimental results demonstrate that the proposed approach achieves superior performance to state-of-the-art techniques in detecting forgeries, even in small patches, in JPEG compressed images, for both median filtered and antiforensic median filtered images.