  Wide deployment of deep neural networks (DNNs) based applications (e.g.,
style transfer, cartoonish), stimulating the requirement of copyright
protection of such application's production. Although some traditional visible
copyright techniques are available, they would introduce undesired traces and
result in a poor user experience. In this paper, we propose a novel
plug-and-play invisible copyright protection method based on defensive
perturbation for DNN-based applications (i.e., style transfer). Rather than
apply the perturbation to attack the DNNs model, we explore the potential
utilization of perturbation in copyright protection. Specifically, we project
the copyright information to the defensive perturbation with the designed
copyright encoder, which is added to the image to be protected. Then, we
extract the copyright information from the encoded copyrighted image with the
devised copyright decoder. Furthermore, we use a robustness module to
strengthen the decoding capability of the decoder toward images with various
distortions (e.g., JPEG compression), which may be occurred when the user posts
the image on social media. To ensure the image quality of encoded images and
decoded copyright images, a loss function was elaborately devised. Objective
and subjective experiment results demonstrate the effectiveness of the proposed
method. We have also conducted physical world tests on social media (i.e.,
Wechat and Twitter) by posting encoded copyright images. The results show that
the copyright information in the encoded image saved from social media can
still be correctly extracted.

Computer Vision and Pattern Recognition;Multimedia