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.

翻译：深度神经网络（DNN）应用（如图像风格迁移、卡通化处理）的广泛部署，使得保护此类应用生成内容的版权需求日益迫切。尽管已有一些传统可见版权技术，但它们会引入不必要的痕迹并导致用户体验下降。本文提出了一种基于防御性扰动的即插即用型不可见版权保护方法，专门针对DNN应用（以风格迁移为例）。与利用扰动攻击DNN模型不同，我们探索了扰动在版权保护中的潜在应用。具体而言，我们通过设计的版权编码器将版权信息投影至防御性扰动中，并将该扰动添加至待保护图像；随后利用设计的版权解码器从编码后的受版权保护图像中提取版权信息。此外，我们引入鲁棒性模块增强解码器对各类图像失真（如JPEG压缩）的解码能力，这些失真可能发生在用户将图像发布至社交媒体时。为保障编码图像与解码版权图像的画质，我们精心设计了损失函数。客观与主观实验结果验证了所提方法的有效性。我们还在社交媒体平台（微信和Twitter）上进行了物理世界测试，通过发布编码后的版权图像，结果表明从社交媒体保存的编码图像中仍能正确提取版权信息。