A natural image model approach to splicing detection
A natural image model approach to splicing detection
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DOI：
10.1145/1288869.1288878
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Y. Shi;Chunhua Chen;Wen Chen
中科院分区：
文献类型：
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作者：
Y. Shi;Chunhua Chen;Wen Chen
图像拼接检测在数字取证中具有重要意义，近年来受到了广泛的关注。在本文中，我们提出了一个盲目的，被动的，但有效的拼接检测方法的基础上的自然图像模型。该自然图像模型由从给定的测试图像中提取的统计特征以及通过对测试图像应用多尺寸块离散余弦变换（MBDCT）生成的2-D阵列组成。统计特征包括小波子带特征函数的矩和差分二维阵列的马尔可夫转移概率。为了评估我们所提出的模型的性能，我们进一步提出了一个具体的实现，该模型已被设计并应用于哥伦比亚图像拼接检测评估数据集。我们的实验工作表明，这种新的剪接检测方案优于现有技术的显着保证金时，应用于上述数据集，表明所提出的方法具有良好的剪接检测能力。
Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include moments of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection.