Identifying Computer Generated Images Based on Quaternion Central Moments in Color Quaternion Wavelet Domain
Identifying Computer Generated Images Based on Quaternion Central Moments in Color Quaternion Wavelet Domain
复制标题
基于彩色四元数小波域四元数中心矩的计算机生成图像识别
DOI：
10.1109/tcsvt.2018.2867786
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发表时间：
2019-09-01
影响因子：
8.4
通讯作者：
Jha, Sunil Kr.
中科院分区：
文献类型：
--
作者：
Wang, Jinwei;Li, Ting;Jha, Sunil Kr.
提出了一种基于彩色四元数小波变换（CQWT）域的彩色图像取证方案。与离散小波变换（DWT）、轮廓小波变换（contourlet wavelet transform）和局部二值模式（local binary patterns）相比，CQWT将一幅彩色图像作为一个单元来处理，因此，通过考虑四元数的幅度和相位度量，CQWT可以为识别照片（PG）和计算机生成（CG）图像提供更多的证据信息。同时，提出了两个新的四元数中心矩，即四元数偏度和峰度，用于提取彩色图像的取证特征。在统计模型与Farid模型相同的情况下，CQWT可以提高现有识别模型的性能。与7500 PG和7500 CG中的Farid模型和Li模型相比，四元数统计特征显示出更好的分类性能。对比实验结果表明，CQWT的分类准确率比Farid模型提高了19%，四元数特征比传统模型提高了约2%。
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.