Abstract
Recent studies have indicated that Convolutional Neural Network (CNN), incorporated with certain domain knowledge, is capable of achieving competitive performances on discriminating trivial perturbation introduced by spatial steganographic schemes. In this paper, we propose a deep residual multi-scale convolutional network model, which outperforms several CNN-based steganalysis schemes and hand-crafted rich models. Compared to CNN-based steganalyzers proposed in recent studies, our model has a deeper network structure and it is integrated with a series of proven elements and complicated convolutional modules. With the intention of abstracting features from various dimensions, multi-scale convolutional modules are designed in three different ways. Besides, inspired by the idea of residual learning, shortcut components are adopted in the proposed model. Extensive experiments with BOSSbase v1.01 and LIRMMBase are carried out, which demonstrates that our network is able to detect multiple state-of-the-art spatial embedding schemes with different payloads.
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Acknowledgments
This work was supported by NSFC under 61802393, U1636102, U1736214 and 61872356, National Key Technology R&D Program under 2016YFB0801003 and 2016QY15Z2500, and Project of Beijing Municipal Science & Technology Commission under Z181100002718001.
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Zhang, S., Zhang, H., Zhao, X., Yu, H. (2019). A Deep Residual Multi-scale Convolutional Network for Spatial Steganalysis. In: Yoo, C., Shi, YQ., Kim, H., Piva, A., Kim, G. (eds) Digital Forensics and Watermarking. IWDW 2018. Lecture Notes in Computer Science(), vol 11378. Springer, Cham. https://doi.org/10.1007/978-3-030-11389-6_4
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