Deep Learning for Steganalysis via Convolutional Neural Networks
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 | February 8-12, 2015 |
| 会议地点 | San Francisco, USA |
| 关键词 | Steganalysis Deep Learning |
| 英文摘要 | Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
| 会议录 | Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics 2015
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12305]  |
| 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 |
| 推荐引用方式 GB/T 7714 | Qian, Yinlong,Dong, Jing,Wang, Wei,et al. Deep Learning for Steganalysis via Convolutional Neural Networks[C]. 见:. San Francisco, USA. February 8-12, 2015. |
入库方式： OAI收割
来源：自动化研究所
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