  Recently, the application of deep learning in steganalysis has drawn many
researchers' attention. Most of the proposed steganalytic deep learning models
are derived from neural networks applied in computer vision. These kinds of
neural networks have distinguished performance. However, all these kinds of
back-propagation based neural networks may be cheated by forging input named
the adversarial example. In this paper we propose a method to generate
steganographic adversarial example in order to enhance the steganographic
security of existing algorithms. These adversarial examples can increase the
detection error of steganalytic CNN. The experiments prove the effectiveness of
the proposed method.
