An invisible, robust copyright protection method for DNN-generated content

Abstract

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the need for copyright protection of such application’s production. Though some traditional visible copyright techniques exist, they often introduce undesired artifacts and compromise the aesthetic quality of the images. In this paper, we propose a novel invisible, robust copyright protection method, which is composed of two networks: the copyright encoder and the copyright decoder. The former projects the copyright information to the invisible perturbation with the drive of both the input of images and copyright information, thereby adding it to the image and yielding encoded images. The copyright decoder extracts copyright information from encoded images. Moreover, a robustness module is integrated to enhance the decoder’s ability to decipher images against various distortions encountered on social media platforms. Furthermore, the loss function is elaborately designed, taking into account both feature space and color space, to guarantee the quality of encoded and decoded copyright images. Extensively objective and subjective experiments validate the effectiveness of the proposed method. Additionally, the physical test is conducted by posting the encoded images to social media (e.g., Weibo and Twitter) and downloading them to verify the feasibility of the proposed method in practice.