  Convolutional Neural Networks (CNN) based methods have significantly improved
the performance of image steganalysis compared with conventional ones based on
hand-crafted features. However, many existing literatures on computer vision
have pointed out that those effective CNN-based methods can be easily fooled by
adversarial examples. In this paper, we propose a novel steganography framework
based on adversarial example in an iterative manner. The proposed framework
first starts from an existing embedding cost, such as J-UNIWARD in this work,
and then updates the cost iteratively based on adversarial examples derived
from a series of steganalytic networks until achieving satisfactory results. We
carefully analyze two important factors that would affect the security
performance of the proposed framework, i.e. the percentage of selected
gradients with larger amplitude and the adversarial intensity to modify
embedding cost. The experimental results evaluated on three modern steganalytic
models, including GFR, SCA-GFR and SRNet, show that the proposed framework is
very promising to enhance the security performances of JPEG steganography.
