吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于分形维数和小波域特征的计算机生成图像检测算法

孙利1, 齐嘉锐2, 李岩波3, 万国富4   

  1. 1. 吉林广播电视大学, 长春 130022; 2. 吉林大学 软件学院, 长春 130012;3. 吉林大学 数学学院, 长春 130012; 4. 吉林大学 计算机科学与技术学
    院, 长春 130012
  • 收稿日期:2012-12-19 出版日期:2013-07-26 发布日期:2013-08-06
  • 通讯作者: 万国富 E-mail:gfwan10@mails.jlu.edu.cn

Detection of Computer Generated Images Based onFractal Dimension and Wavelet Domain Features

SUN Li1, QI Jia |rui2, LI Yan bo3, WAN Guofu4   

  1. 1. Jilin Radio and TV University, Changchun 130022, China;2. College of Software, Jilin University, Changchun 130012, China;3. College of Mathematics,  Jilin University, Changchun 130012, China;4. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2012-12-19 Online:2013-07-26 Published:2013-08-06
  • Contact: WAN Guofu E-mail:gfwan10@mails.jlu.edu.cn

摘要:

针对自然图像与计算机生成图像在统计特征上存在的差异, 提出一种基于分形维数和小波域特征的计算机生成图像盲鉴别算法, 该算法基于统计特征对图像进行真伪识别. 在使用支持向量机作为分类器的情况下, 对800张标准图像进行实验的结果表明, 该算法对计算机生成图像检测准确率达96.5%, 明显提高了计算机生成图像的识别精度, 为数字图像的真实性提供了保证.

关键词: 自然图像, 计算机生成图像, 小波域, 分形维数, 支持向量机

Abstract:

In view of the perceptual differences between photographic and computer generated images primarily existed in statistical features, an identification algorithm for photographic and computer generated images was proposed, which is based on fractal dimension and wavelet domain features. The algorithm that is based on the statistical characteristics was used to recognize the authenticity of images. In the process of our experiment, 800 standard images were used as the image database, the support vector machine was used as the classifier, and the detection accuracy rate of the computer generated images could reach 96.5% in the algorithm. It has significantly improved the accuracy of identifying computer generated images, and provides a guarantee of the authenticity of the digital image.

Key words: photographic image, computer generated image, wavelet domain, fractal dimension, support vector machine

中图分类号: 

  • TP391