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Proceedings Paper

Deep learning for steganalysis via convolutional neural networks
Author(s): Yinlong Qian; Jing Dong; Wei Wang; Tieniu Tan
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Paper Abstract

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.

Paper Details

Date Published: 4 March 2015
PDF: 10 pages
Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090J (4 March 2015); doi: 10.1117/12.2083479
Show Author Affiliations
Yinlong Qian, Univ. of Science and Technology of China (China)
Institute of Automation (China)
Jing Dong, Institute of Automation (China)
Wei Wang, Institute of Automation (China)
Tieniu Tan, Institute of Automation (China)

Published in SPIE Proceedings Vol. 9409:
Media Watermarking, Security, and Forensics 2015
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)

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