DIGITAL IMAGE FORENSICS USING STATISTICAL FEATURES AND NEURAL NETWORK CLASSIFIER

摘要：

Digital image forensics is a new topic in recent years, which deals with the authenticity and credibility of digital images. How to recognize fake images is still a problem. This paper presents a fake image classification scheme using higher order image statistics and KBF neural networks. The features constructed on the higher order statistics reveal the intrinsic statistical features between fake images and real images. Then a classifier based on RBF neural networks is used to classify the fake and real images using these features. Experimental results demonstrated the effectiveness of the proposed scheme.

关键词： Digital Image Forensics Higher Order Autocorrelation Statistics RBF Neural Network

作者: WEI LU WEI SUN JI-WU HUANG HONG-TAO LU

作者单位: School of Information Science and Technology and Guangdong Key Laboratory of Information Security Te School of Software and Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen Univ Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, Chin

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2831-2834

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）