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首页> 外文期刊>Journal of electronic imaging >Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion
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Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion

机译：基于最大化和阈值扩展的高效马尔可夫特征提取方法

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摘要

We propose an efficient Markov feature extraction method for color image splicing detection. The maximum value among the various directional difference values in the discrete cosine transform domain of three color channels is used to choose the Markov features. We show that the discriminability for slicing detection is increased through the maximization process from the point of view of the Kullback-Leibler divergence. In addition, we present a threshold expansion and Markov state decomposition algorithm. Threshold expansion reduces the information loss caused by the coefficient thresholding that is used to restrict the number of Markov features. To compensate the increased number of features due to the threshold expansion, we propose an even-odd Markov state decomposition algorithm. A fixed number of features, regardless of the difference directions, color channels and test datasets, are used in the proposed algorithm. We introduce three kinds of Markov feature vectors. The number of Markov features for splicing detection used in this paper is relatively small compared to the conventional methods, and our method does not require additional feature reduction algorithms. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection. (C) 2016 SPIE and IS&T
机译：我们提出了一种有效的马尔可夫特征提取方法用于彩色图像拼接检测。使用三个颜色通道的离散余弦变换域中各个方向差值中的最大值来选择马尔可夫特征。我们显示，从Kullback-Leibler散度的角度来看，通过最大化过程可以提高切片检测的可分辨性。此外，我们提出了阈值扩展和马尔可夫状态分解算法。阈值扩展减少了因系数阈值而引起的信息丢失，该系数阈值用于限制Markov特征的数量。为了补偿由于阈值扩展而增加的特征数量，我们提出了奇偶马尔可夫状态分解算法。所提出的算法中使用了固定数量的特征，而不管差异方向，颜色通道和测试数据集如何。我们介绍了三种马尔可夫特征向量。与传统方法相比，本文使用的马尔可夫特征进行拼接检测的数量相对较少，并且我们的方法不需要其他特征缩减算法。通过实验仿真，我们证明了该方法在拼接检测中具有较高的性能。 （C）2016 SPIE和IS＆T

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