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Learning Representations for Steganalysis from Regularized CNN Model with Auxiliary Tasks
Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu
2015
会议名称International Conference on Communications, Signal Processing, and Systems
会议录名称Proceedings of International Conference on Communications, Signal Processing, and Systems
会议日期Oct. 23-24, 2015
会议地点Chengdu, China
摘要The key challenge of steganalysis is to construct effective feature representations. Traditional steganalysis systems rely on hand-designed feature extractors. Recently, some efforts have been put toward learning representations automatically using deep models. In this paper, we propose a new CNN based framework for steganalysis based on the concept of incorporating prior knowledge from auxiliary tasks via transfer learning to regularize the CNN model for learning better representations. The auxiliary tasks are generated by computing features that capture global image statistics which are hard to be seized by the CNN network structure. By detecting representative modern embedding methods, we demonstrate that the proposed method is effective in improving the feature learning in CNN models.
关键词Steganalysis Regularized Cnn
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12307
专题智能感知与计算研究中心
通讯作者Dong, Jing
作者单位Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Qian, Yinlong,Dong, Jing,Wang, Wei,et al. Learning Representations for Steganalysis from Regularized CNN Model with Auxiliary Tasks[C],2015.
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