  Recent studies shows that the majority of existing deep steganalysis models
have a large amount of redundancy, which leads to a huge waste of storage and
computing resources. The existing model compression method cannot flexibly
compress the convolutional layer in residual shortcut block so that a
satisfactory shrinking rate cannot be obtained. In this paper, we propose
STD-NET, an unsupervised deep-learning architecture search approach via
hierarchical tensor decomposition for image steganalysis. Our proposed strategy
will not be restricted by various residual connections, since this strategy
does not change the number of input and output channels of the convolution
block. We propose a normalized distortion threshold to evaluate the sensitivity
of each involved convolutional layer of the base model to guide STD-NET to
compress target network in an efficient and unsupervised approach, and obtain
two network structures of different shapes with low computation cost and
similar performance compared with the original one. Extensive experiments have
confirmed that, on one hand, our model can achieve comparable or even better
detection performance in various steganalytic scenarios due to the great
adaptivity of the obtained network architecture. On the other hand, the
experimental results also demonstrate that our proposed strategy is more
efficient and can remove more redundancy compared with previous steganalytic
network compression methods.
