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Image Splicing detection based on image quality and analysis of variance

机译:基于图像质量和方差分析的图像拼接检测

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In this paper an image splicing detection scheme is proposed. The scheme is based on image quality and analysis of variance. Four kinds of noise used to simulated the image quality changes which caused by tampering of images, and analysis of variance is used to selected the image quality measures which are more sensitive to image blind splicing detection. Combined with the characteristic function moments of threelevel wavelet sub-bands and the further decomposition coefficients of the first scale diagonal sub-band, we extracted all features from given image and it's predicted error image. SVM is adopted as the classifier to train and test the given images. The simulation results show the proposed scheme has good performance in the average detection accuracy increased by about 1.5% ~ 15% than the existed methods.
机译:本文提出了一种图像拼接检测方案。该方案基于图像质量和方差分析。四种噪声被用来模拟由图像篡改引起的图像质量变化,并使用方差分析来选择对图像盲拼接检测更为敏感的图像质量度量。结合三级小波子带的特征函数矩和第一尺度对角线子带的进一步分解系数,我们从给定图像及其预测误差图像中提取了所有特征。采用SVM作为分类器来训练和测试给定的图像。仿真结果表明,该方案在平均检测精度上比现有方法提高了约1.5%〜15%,具有良好的性能。

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