Imperceptible digital watermarking is important in copyright protection, misinformation prevention, and responsible generative AI. We propose TrustMark - a GAN-based watermarking method with novel design in architecture and spatio-spectra losses to balance the trade-off between watermarked image quality with the watermark recovery accuracy. Our model is trained with robustness in mind, withstanding various in- and out-place perturbations on the encoded image. Additionally, we introduce TrustMark-RM - a watermark remover method useful for re-watermarking. Our methods achieve state-of-art performance on 3 benchmarks comprising arbitrary resolution images.

翻译：不可感知的数字水印技术在版权保护、虚假信息防范及负责任生成式人工智能领域具有重要意义。本文提出TrustMark——一种基于生成对抗网络的数字水印方法，通过创新性的架构设计与空谱联合损失函数，有效平衡了水印图像质量与水印恢复精度之间的权衡关系。该模型在训练过程中充分考虑了鲁棒性要求，能够抵御对编码图像施加的多种原位及非原位扰动。此外，我们进一步提出TrustMark-RM水印移除方法，可用于实现水印重置操作。在包含任意分辨率图像的三个基准测试中，所提方法均取得了当前最优性能。