Blind Forensics of Median Filtering Based on Markov Statistics in Median-Filtered Residual Domain
Blind Forensics of Median Filtering Based on Markov Statistics in Median-Filtered Residual Domain
复制标题
基于中值滤波残差域马尔可夫统计的中值滤波盲取证
DOI：
10.1007/978-3-319-00536-2_21
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Li Shenghong
中科院分区：
文献类型：
--
作者：
Zhang Yujin;Zhao Chenglin;Li Shenghong
近年来，揭示数字图像的处理历史受到了法医分析者的极大关注。中值滤波是一种非线性运算，已被广泛应用于图像去噪和图像增强。因此，暴露此类操作带来的痕迹有助于法医分析人员。提出了一种检测数字图像中值滤波的被动取证方法。由于重叠窗滤波引入了中值滤波残差(MFR)元素之间的相关性，即测试图像与其相应的中值滤波版本之间的差，因此从MFR计算沿水平、垂直、主对角线和次对角线方向的转移概率矩阵来表征MFR元素之间的相关性。这些转移概率矩阵的所有元素都被用作中值滤波检测的判别特征。实验结果证明了该方法的有效性。
Revealing the processing history of a digital image has received a great deal of attention from forensic analyzers in recent years. Median filtering is a non-linear operation and has been used widely for noise removal and image enhancement. Therefore, exposing the traces introduced by such operation is helpful to forensic analyzers. In this paper, a passive forensic method to detect median filtering in digital images is proposed. Since overlapped window filtering introduces the correlation among the elements of the median-filtered residual (MFR) which is referred to as the difference between a test image and its corresponding median-filtered version, the transition probability matrices along the horizontal, vertical, main diagonal and minor diagonal directions are calculated from the MFR to characterize the correlation among the elements of the MFR. All elements of these transition probability matrices are served as discriminative features for median filtering detection. Experiment results demonstrate the effectiveness of the proposed method.
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DOI：
--
发表时间：
2012-12
期刊：
Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference
影响因子：
--
作者：
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通讯作者：
Xiangui Kang;M. Stamm;Anjie Peng;K. Liu
DOI：
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期刊：
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2007-09
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通讯作者：
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影响因子：
10.6
作者：
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通讯作者：
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DOI：
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发表时间：
2008-12
影响因子：
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作者：
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通讯作者：
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