Structural design of convolutional neural networks for steganalysis

Document Type

Article

Publication Date

5-1-2016

Abstract

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1 × 1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.

Identifier

84964992861 (Scopus)

Publication Title

IEEE Signal Processing Letters

External Full Text Location

https://doi.org/10.1109/LSP.2016.2548421

ISSN

10709908

First Page

708

Last Page

712

Issue

5

Volume

23

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