Deep Learning for Steganalysis via Convolutional Neural Networks | |
Qian, Yinlong; Dong, Jing![]() ![]() ![]() | |
2015 | |
会议名称 | SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics |
会议录名称 | Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics 2015 |
会议日期 | February 8-12, 2015 |
会议地点 | San Francisco, USA |
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. |
关键词 | Steganalysis Deep Learning |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12305 |
专题 | 模式识别实验室 |
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],2015. |
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Deep learning for st(248KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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