WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

TL;DR

WISERNet is a novel deep neural network designed for color image steganalysis, employing a wider separate-then-reunion structure that outperforms existing models in detection accuracy while maintaining lower complexity.

Contribution

The paper introduces WISERNet, a wider separate-then-reunion network that leverages channel-wise convolution in lower layers and normal convolution in upper layers for improved color image steganalysis.

Findings

Outperforms state-of-the-art models in detection accuracy

Achieves higher performance with less than half the complexity

Effective across various datasets and image processing algorithms

Abstract

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with different demosaicking algorithms and down-sampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deep-learning steganalyzer as far as we know, which is scarce in the literature.