  Nowadays a steganography has to face challenges of both feature based
staganalysis and convolutional neural network (CNN) based steganalysis. In this
paper, we present a novel steganography scheme denoted as ITE-SYN (based on
ITEratively adversarial perturbations onto a SYNchronized-directions
sub-image), by which security data is embedded with synchronizing modification
directions to enhance security and then iteratively increased perturbations are
added onto a sub-image to reduce loss with cover class label of the target CNN
classifier. Firstly an exist steganographic function is employed to compute
initial costs. Then the cover image is decomposed into some non-overlapped
sub-images. After each sub-image is embedded, costs will be adjusted following
clustering modification directions profile. And then the next sub-image will be
embedded with adjusted costs until all secret data has been embedded. If the
target CNN classifier does not discriminate the stego image as a cover image,
based on adjusted costs, we change costs with adversarial manners according to
signs of gradients back-propagated from the CNN classifier. And then a
sub-image is chosen to be re-embedded with changed costs. Adversarial intensity
will be iteratively increased until the adversarial stego image can fool the
target CNN classifier. Experiments demonstrate that the proposed method
effectively enhances security to counter both conventional feature-based
classifiers and CNN classifiers, even other non-target CNN classifiers.
