  Over the past few years, detection performance improvements of deep-learning
based steganalyzers have been usually achieved through structure expansion.
However, excessive expanded structure results in huge computational cost,
storage overheads, and consequently difficulty in training and deployment. In
this paper we propose CALPA-NET, a ChAnneL-Pruning-Assisted deep residual
network architecture search approach to shrink the network structure of
existing vast, over-parameterized deep-learning based steganalyzers. We observe
that the broad inverted-pyramid structure of existing deep-learning based
steganalyzers might contradict the well-established model diversity oriented
philosophy, and therefore is not suitable for steganalysis. Then a hybrid
criterion combined with two network pruning schemes is introduced to adaptively
shrink every involved convolutional layer in a data-driven manner. The
resulting network architecture presents a slender bottleneck-like structure. We
have conducted extensive experiments on BOSSBase+BOWS2 dataset, more diverse
ALASKA dataset and even a large-scale subset extracted from ImageNet CLS-LOC
dataset. The experimental results show that the model structure generated by
our proposed CALPA-NET can achieve comparative performance with less than two
percent of parameters and about one third FLOPs compared to the original
steganalytic model. The new model possesses even better adaptivity,
transferability, and scalability.
