CASIA OpenIR  > 智能感知与计算研究中心
An Adaptive Ensemble Classifier for Steganalysis Based on Dynamic Weighted Fusion
Xikai Xu; Jing Dong; Wei Wang; Tieniu Tan
2015
Conference NameInternational Conference on Communications, Signal Processing, and Systems
Source PublicationProceedings of the 2015 International Conference on Communications, Signal Processing, and Systems
Conference DateOctober 23–24, 2015
Conference PlaceChengdu, China
AbstractRecently, ensemble classifier is predominantly used for steganalysis of digital media, due to its efficiency when working with high-dimensional feature sets and large databases. While fusing the decisions of many weak base classifiers, the majority voting rule is often used, which has the disadvantage that all the classifiers have the same authority regardless of their individual classification abilities. In this paper, we propose a new dynamic weighted fusion method for steganalysis which can be adaptive to input testing samples. For each testing sample, the weight of each base classifier is dynamically assigned according to the distance between the testing sample and the classifier. Experimental results show that the proposed method is able to increase steganalysis performance.
KeywordSteganalysis
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12567
Collection智能感知与计算研究中心
Corresponding AuthorJing Dong
Recommended Citation
GB/T 7714
Xikai Xu,Jing Dong,Wei Wang,et al. An Adaptive Ensemble Classifier for Steganalysis Based on Dynamic Weighted Fusion[C],2015.
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