Image splicing detection based on image quality and analysis of variance
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 three-level 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.