CASIA OpenIR  > 智能感知与计算研究中心
Deep Learning for Steganalysis via Convolutional Neural Networks
Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu
2015
Conference NameSPIE, Electronic Imaging, Media Watermarking, Security, and Forensics
Source PublicationProc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics 2015
Conference DateFebruary 8-12, 2015
Conference PlaceSan Francisco, USA
AbstractCurrent 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.
KeywordSteganalysis Deep Learning
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12305
Collection智能感知与计算研究中心
Corresponding AuthorDong, Jing
Recommended Citation
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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