Learning Representations for Steganalysis from Regularized CNN Model with Auxiliary Tasks
文献类型：会议论文
| 作者 | Qian, Yinlong; Dong, Jing ; Wang, Wei; Tan, Tieniu
|
| 出版日期 | 2015 |
| 会议日期 | Oct. 23-24, 2015 |
| 会议地点 | Chengdu, China |
| 关键词 | Steganalysis Regularized Cnn |
| 英文摘要 | 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. |
| 会议录 | Proceedings of International Conference on Communications, Signal Processing, and Systems
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12307]  |
| 专题 | 自动化研究所_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]. 见:. Chengdu, China. Oct. 23-24, 2015. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。