Universal Deep Network for Steganalysis of Color Image based on Channel Representation

TL;DR

This paper introduces UCNet, a universal deep learning model for color image steganalysis that effectively detects hidden information in both spatial and JPEG domains, outperforming existing CNN-based methods.

Contribution

The paper proposes a novel CNN architecture tailored for color images, with specialized preprocessing and convolutional modules, achieving state-of-the-art results in steganalysis.

Findings

Achieves superior detection accuracy on ALASKA II dataset.

Reduces training time and memory usage compared to prior CNN steganalyzers.

Effective in both spatial and JPEG steganalysis domains.

Abstract

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design.