Detection of Image Compositing Based on a Statistical Model for Natural Images

作     者：SUN Shao-Jie WU Qiong LI Guo-Hui 

作者机构：College of Information System and Management National University of Defense Technology Changsha 410073 P. R. China 

出 版 物：《自动化学报》 (Acta Automatica Sinica)

年 卷 期：2009年第35卷第12期

页      面：1564-1567页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

主　　题：图像处理技术 图像合成 数字干扰 软件 

摘      要：Nowadays,digital images can be easily tampered due to the availability of powerful image processing software. As digital cameras continue to replace their analog counterparts, the importance of authenticating digital images,identifying their sources,and detecting forgeries is *** image forensies is used to analyze an image in the complete absence of any digital watermark or *** compositing is the most common form of digital *** that image compositing operations affect the inherent statistics of the image,we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform *** generalized Gaussian model(GGD)is employed to describe the marginal distribution of wavelet coefficients of images,and the parameters of GGD are obtained using maximum likelihood *** statistical features include GGD parameters,prediction error,mean,variance,skewness,and kurtosis at each wavelet detail ***,these feature vectors are used to discriminate between natural images and composite images using support vector machine(SVM).To evaluate the performance of our proposed method,we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset,and achieved a detection accuracy of 92% and 79%.*** detection performance of our method is better than that of the method using cameraresponse function on the same dataset.