基于协方差矩阵的CFA插值盲检测方法
doi: 10.3724/SP.J.1146.2008.00146
Blind CFA Interpolation Detection Based on Covariance Matrix
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摘要: 从数字图像中盲检测数码相机采用的CFA插值算法,可以为数字图像取证提供重要的技术手段。该文基于线性插值模型,利用协方差矩阵构建插值系数方程组,并将估计的插值系数构成特征向量空间,采用支持向量机作为分类工具,提出了一种对不同的CFA插值算法进行准确分类的盲检测方法。实验表明,该文方法对于不同的CFA插值算法均能达到较高检测正确率。同时,相比现有的CFA插值检测方法,该文算法对加性高斯白噪声和有损JPEG压缩具有更好的鲁棒性。Abstract: Blind CFA interpolation detection, which identifies the demosaicing method used in digital camera by analyzing output images, provides an efficient tool for digital image forensics. This paper proposes an approach of blind CFA interpolation detection based on interpolation coefficients estimation. By solving the covariance matrix equation, a vector of the interpolation coefficients is obtained, which is further fed to SVM classifier. The experimental results show a high accuracy on blind CFA interpolation detection. Compared with existing ones, the proposed method in this paper indicates a better performance on the robustness against additive Gaussian white noises and lossy JPEG compression.
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