  For steganalysis, many studies showed that convolutional neural network has
better performances than the two-part structure of traditional machine learning
methods. However, there are still two problems to be resolved: cutting down
signal to noise ratio of the steganalysis feature map and steganalyzing images
of arbitrary size. Some algorithms required fixed size images as the input and
had low accuracy due to the underutilization of the noise residuals obtained by
various types of filters. In this paper, we focus on designing an improved
network structure based on CNN to resolve the above problems. First, we use 3x3
kernels instead of the traditional 5x5 kernels and optimize convolution kernels
in the preprocessing layer. The smaller convolution kernels are used to reduce
the number of parameters and model the features in a small local region. Next,
we use separable convolutions to utilize channel correlation of the residuals,
compress the image content and increase the signal-to-noise ratio (between the
stego signal and the image signal). Then, we use spatial pyramid pooling (SPP)
to aggregate the local features, enhance the representation ability of
features, and steganalyze arbitrary size image. Finally, data augmentation is
adopted to further improve network performance. The experimental results show
that the proposed CNN structure is significantly better than other four methods
such as SRM, Ye-Net, Xu-Net, and Yedroudj-Net, when it is used to detect two
spatial algorithms such as WOW and S-UNIWARAD with a wide variety of datasets
and payloads.
