  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.

Computer Vision and Pattern Recognition;Cryptography and Security