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Deep Learning for Steganalysis via Convolutional Neural Networks
Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu
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],2015.
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