Median filtering detection using LBP encoding pattern

作     者：Wang, Xiaofeng Li, Xinai Tang, Chao Ding, Liangyiwen 

作者机构：Xian Univ Technol Sch Sci Xian 710048 Shaanxi Peoples R China 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (Multimedia Tools Appl)

年 卷 期：2022年

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61772416, 61973094] Key Laboratory Project of the Education Department of Shaanxi Province [17JS098] Shaanxi province technology innovation guiding fund project [2018XNCG-G-G-02] Shaanxi Science Foundation of China [2022GY-087] 

主　　题：Median filtering detection Local binary pattern Discriminating algorithm Recognition algorithm 

摘      要：In recent years, median filtering detection has a widely application in many fields such as images processing history tracking, image editing detection, image anti-forensics analyzing and anti-steganalysis analyzing. In this paper, we propose two median filtering detection algorithms. The Algorithm I is a recognition algorithm that can identify whether a given image has undergone median filtering. The Algorithm II is a discriminating algorithm that can distinguish a median (average, Gaussian) filtered image from unfiltered images. Differing from the general framework of existing median filtering detectors, the contribution of our work is that the presented methods are not based on the statistical learning model. The proposed methods do not need any classifier, or any threshold. These methods are implemented by counting the number of specific Local Binary Pattern encoding patterns of a single image. Experimental results demonstrate that the proposed methods provide high accuracy and broad-spectrum robustness for tolerating content-preserving manipulations. Compared to state-of-the-art methods, the proposed methods exhibit high efficiency, high accuracy, and strong robustness.