  Steganalysis as a method to detect whether image contains se-cret message, is
a crucial study avoiding the imperils from abus-ing steganography. The point of
steganalysis is to detect the weak embedding signals which is hardly learned by
convolution-al layer and easily suppressed. In this paper, to enhance
embed-ding signals, we study the insufficiencies of activation function,
filters and loss function from the aspects of reduce embedding signal loss and
enhance embedding signal capture ability. Adap-tive Parametric Activation
Module is designed to reserve nega-tive embedding signal. For embedding signal
capture ability enhancement, we add constraints on the high-pass filters to
im-prove residual diversity which enables the filters extracts rich embedding
signals. Besides, a loss function based on contrastive learning is applied to
overcome the limitations of cross-entropy loss by maximum inter-class distance.
It helps the network make a distinction between embedding signals and semantic
edges. We use images from BOSSbase 1.01 and make stegos by WOW and S-UNIWARD
for experiments. Compared to state-of-the-art methods, our method has a
competitive performance.
