Blind image forensics based on dual-tree complex wavelet transform statistical features

Comparing Dual Tree Complex Wavelet Transform with conventional Discrete Wavelet Transform supports that Dual Tree Complex Wavelet Transform has some advances including shift invariance and direction selection. Multi-scale decomposition of the image could be formed using Dual Tree Complex Wavelet Transform, and the first to fifth central moment of the amplitudes of wavelet coefficients and forecasting error was calculated individually based on six direction complex high-frequency subbands at each level of Multi-scale construction, then the statistical feature vector was composed of these moments. The set of statistical feature vectors of photorealism images and computer generation images could be extracted using the proposed feature vector extraction method, and then support vector machine could be used for the classification purpose. As result of the experiment, the corresponding accurate identification rate is 98% and 97% for photorealism images and computer generation images from the test sets individually. These results show that better classification performance of the proposed algorithm.