%0 Journal Article
%T Passive image splicing detection based on third order statistical features<br>基于三阶统计特征的被动图像拼接检测
%A ZHAO Xu-dong
%A YUAN Ye
%A LI Sheng-hong
%A WANG Shi-lin
%A LI Jian-hua
%A <br>赵旭东
%A 袁　野
%A 李生红
%A 王士林
%A 李建华
%J 计算机应用研究
%D 2012
%I 
%X This paper proposed a third order statistical features based method to detect image splicing operation passively. The dependences among neighboring three states in the state-array were modeled as CCPM which was treated as discriminative features for SVM classification. Since the dimensionality of higher order statistical features grows exponentially with the order, it introduced PCA to decrease the complexity for classification and to overcome the potential over-fitting problem. Experimental results show that the conditional co-occurrence probability matrix features outperform traditional Markov features and gray level co-occurrence matrix features in both 8×8 block DCT domain and spatial domain. PCA is an effective tool for image splicing detection and new features with much fewer dimensionalities after PCA perform as good as original features.
%K image splicing detection
%K third order statistical features
%K conditional co-occurrence probability matrix（CCPM）
%K principal component analysis（PCA）
%K support vector machine（SVM）<br>图像拼接检测
%K 三阶统计特征
%K 条件共生概率矩阵
%K 主成分分析
%K 支持向量机
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=8D8174D12B4331BF834D61A579ABD193&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=59906B3B2830C2C5&sid=A87EEB62E0265102&eid=A9707E3CA8E199E4&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=21