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A method for identifying computer images and real images

机译：一种识别计算机图像和真实图像的方法

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Based on the differences of pattern noise between real images and computer images, this paper advanced a new method which combined the SNR features with the higher order characteristics of the predicting error images. Where the SNR features consist of MSE, SNR and PSNR features between original images and modified images which were got by add-noising and de-noising for the original images. Experimental results show that this algorithm's recognition rate can get 94.33% in the Columbia Image Dataset [1].

机译：针对真实图像和计算机图像之间的图案噪声差异，提出了一种将信噪比特征与预测误差图像的高阶特征相结合的新方法。 SNR特征包括原始图像和修改后的图像之间的MSE，SNR和PSNR特征，这些特征是通过对原始图像进行加噪和除噪来获得的。实验结果表明，该算法在哥伦比亚图像数据集中的识别率达到94.33％[1]。

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《2011 International Conference on Electronics, Communications and Control 》|2011年|p.638-641|共4页
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Guo Ke; Wang Rangding;
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中图分类通信 ;
关键词
Add-noising and de-noising; SNR features; wavelet transform;

机译：加减噪; SNR特性;小波变换;

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A method for identifying computer images and real images

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