A Universal Steganalysis Algorithm For JPEG Image Based on Selective SVMs Ensemble
Universal steganalysis include feature extraction and steganalyzer design.Most universal steganalysis use Support Vector Machine (SVM) as steganalyzer.However,most SVM-based universal steganalysis are not to be very much effective at lower embedding rates.The reason why selective SVMs enscmble improve the generalization ability was analyzed,and an algorithm to select a part of individual SVMs according to their difference to build the ensemble classifier was proposed,which based on the selected ensemble theory-Many could be better than all.In this paper,the selective SVMs ensemble algorithm was used to construct a strong steganalyzer to improve the performance of steganographic detection.The twenty five experiments on the benchmark with 2000 different types of images show that:for popular steganography methods,and under different conditions of embedding rate,the average detection rate of proposed steganalysis method outperforms the maximum average detection rate for the steganalysis method based on single SVM with improving by 3.05%-12.05%; and for the steganalysis method based on BaggingSVM with improving by 0.2%-1.3%.
steganalysis low embedding rate selective ensemble bootstrap SVM
Daya Chen Shangping Zhong
College of Mathematics and Computer Science Fuzhou University Fuzhou, 350108, China
国际会议
西安
英文
1548-1552
2012-08-24（万方平台首次上网日期，不代表论文的发表时间）