基于深度神经网络(DNN)的应用程序的广泛部署(例如,风格转移,卡通),激发了对此类应用程序生产的版权保护的需求。尽管存在一些传统的可见版权技术,它们经常引入不希望的伪影并损害图像的美学质量。在本文中,我们提出了一部看不见的小说,强大的版权保护方法,它由两个网络组成:版权编码器和版权解码器。前者在图像输入和版权信息的驱动下,将版权信息投射到无形的扰动中,从而将其添加到图像并产生编码图像。版权解码器从编码图像中提取版权信息。此外,集成了一个健壮性模块,以增强解码器对社交媒体平台上遇到的各种失真进行破译的图像的能力。此外,损失函数经过精心设计,同时考虑特征空间和颜色空间,保证编码和解码版权图像的质量。广泛的客观和主观实验验证了该方法的有效性。此外,物理测试是通过将编码图像发布到社交媒体来进行的(例如,微博和Twitter)并下载它们,以验证所提出的方法在实践中的可行性。
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.