Deep Learning for Steganalysis via Convolutional Neural Networks

CASIA OpenIR > 模式识别实验室

	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	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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