Identifying Computer Generated Images Based on Quaternion Central Moments in Color Quaternion Wavelet Domain

Abstract

In this paper, a novel forensics scheme for color image is proposed in color quaternion wavelet transform (CQWT) domain. Compared with discrete wavelet transform (DWT), contourlet wavelet transform, and local binary patterns, CQWT processes a color image as a unit, and so, it can provide more forensics information to identify the photograph (PG) and computer generated (CG) images by considering the quaternion magnitude and phase measures. Meanwhile, two novel quaternion central moments for color images, i.e., quaternion skewness and kurtosis, are proposed to extract forensics features. In the condition of the same statistical model as Farid's model, the CQWT can boost the performance of the existing identification models. Compared with Farid's model and Li's model in 7500 PG and 7500 CG, the quaternion statistical features show a better classification performance. Results in the comparative experiments show that the classification accuracy of the CQWT improves by 19% more than Farid's model, and the quaternion features approximately improve by 2% more than the traditional.