基于熵和多步马尔可夫特征的图像拼接检测  

Image Splicing Detection Based on Entropy and Multi-step Markov Feature

作　　者：仝威[1] 赵旭东[1] 王士林[1] 李生红[1] 

机构地区：[1]上海交通大学电子工程系,上海200240

出　　处：《计算机工程》2014年第1期236-238,245,共4页Computer Engineering

摘　　要：随着图形编辑软件的普及,数字图像篡改越来越容易,数字图像篡改检测已成为一个亟需解决的问题。为此,提出基于图片信息熵和多步马尔可夫特征的图像拼接检测方法。该方法将图像拼接检测问题转换为两分类模式识别问题,先从原图、3阶Haar离散小波变换(DWT)和多尺度分块离散余弦变换(DCT)中提取图片的信息熵,再从图像的分块DCT系数中提取多步马尔可夫转移概率矩阵,由信息熵和多步马尔可夫转移概率矩阵组成统计特征,利用支持向量机分类器进行分类得到判决结果。实验结果表明,该方法在哥伦比亚图片库上具有较高的拼接检测精度,达到89.91%。With the popularity of graphic editing software, tampering a digital image becomes more and more easier, so it is urgent to solve digital image forensics problem. Aiming at the problem, this paper proposes an image splicing detection method based on image information entropy feature and multi-step Markov feature. Image splicing detection can be treated as a two-class pattern recognition problem. This method consists of entropy feature extracted from the original image, three-level Haar Discrete Wavelet Transform（DWT） and multiple-size block Discrete Cosine Transform（DCT）, and multi-step Markov feature transition probability matrix is extracted from block DCT. The statistical characteristics consist of information entropy and multi-step Markov feature. Support Vector Machine（SVM） is used to judge the image category and get judgment result. Experimental results show that the proposed method applied to the Columbia image dataset possesses promising capability in splicing detection, and it can achieve a detection accuracy of 89.91%.

关 键 词：数字图像防伪鉴定 拼接检测 信息熵 马尔可夫特征 分块离散余弦变换 支持向量机 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]