Using contrastive learning to improve the performance of steganalysis
  schemes
  To improve the detection accuracy and generalization of steganalysis, this
paper proposes the Steganalysis Contrastive Framework (SCF) based on
contrastive learning. The SCF improves the feature representation of
steganalysis by maximizing the distance between features of samples of
different categories and minimizing the distance between features of samples of
the same category. To decrease the computing complexity of the contrastive loss
in supervised learning, we design a novel Steganalysis Contrastive Loss
(StegCL) based on the equivalence and transitivity of similarity. The StegCL
eliminates the redundant computing in the existing contrastive loss. The
experimental results show that the SCF improves the generalization and
detection accuracy of existing steganalysis DNNs, and the maximum promotion is
2% and 3% respectively. Without decreasing the detection accuracy, the training
time of using the StegCL is 10% of that of using the contrastive loss in
supervised learning.
