基于高频小波子带马尔可夫特征的图像拼接检测  

Image splicing detection based on high frequency wavelet Markov features

作　　者：袁全桥 苏波[1] 赵旭东[2] 李生红[2] 

机构地区：[1]上海交通大学信息安全工程学院,上海200240 [2]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《计算机应用》2014年第5期1477-1481,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61271316;61071152);国家973计划项目(2010CB731403;2010CB731406;2013CB329605);"十二五"国家科技支撑计划项目(2012BAH38 B04)

摘　　要：拼接是图像篡改过程中最普遍使用的操作,通过检测拼接可以有效鉴别图像是否经过人为修改。针对拼接操作提出了一种盲检测方法:首先对图像进行小波变换,在比较分析不同小波子带对图像拼接检测的作用后,选取高频子带作为图像变换域信息;接着对小波子带进行差分操作,并将系数取整阈值化后作为马尔可夫状态;最后计算状态间的转移概率作为拼接特征,利用支持向量机(SVM)进行分类。在哥伦比亚图像拼接评测彩色库和灰度库上分别进行实验,证实了选取小波高频子带提取拼接特征的有效性。通过与其他特征提取方法对比,所提出特征在两个评测库上都表现出了更好的检测效果,尤其在彩色评测库上取得了94.6%的识别率。Splicing is the most universal image tampering operation, detection of which is effective for identifying image tamper. A blind splicing detection method was proposed. The method firstly analyzed the effects of different sub-bands on image splicing detection according to features of wavelet transform. High frequency sub-band was verified to be more appropriate for splicing detection both from theory analysis and experiment results. Secondly, the method conducted difference operation, rounded and made threshold to the coefficients as discrete Markov states, and calculated the state transition probabilities as splicing features. Finally, Support Vector Machine （SVM） was used as classifier, and the features were tested on Columbia image splicing detection evaluation datasets. The experimental results show that the proposed method performs better compared with other features and achieves a detection accuracy rate of 94.6% on the color dataset specially.

关 键 词：离散小波变换 马尔可夫链 转移概率 支持向量机 图像拼接检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]