Deep learning for steganalysis via convolutional neural networks

Yinlong Qian; Jing Dong; Wei Wang; Tieniu Tan

doi:10.1117/12.2083479

4 March 2015 Deep learning for steganalysis via convolutional neural networks

Yinlong Qian, Jing Dong, Wei Wang, Tieniu Tan

Proceedings Volume 9409, Media Watermarking, Security, and Forensics 2015; 94090J (2015) https://doi.org/10.1117/12.2083479
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

Citation Download Citation

Yinlong Qian, Jing Dong, Wei Wang, and Tieniu Tan "Deep learning for steganalysis via convolutional neural networks", Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090J (4 March 2015); https://doi.org/10.1117/12.2083479

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