The advancement of artificial intelligence generated content (AIGC) has created a pressing need for robust image watermarking that can withstand both conventional signal processing and novel semantic editing attacks. Current deep learning-based methods rely on training with hand-crafted noise simulation layers, which inherently limit their generalization to unforeseen distortions. In this work, we propose $\textbf{SimuFreeMark}$, a noise-$\underline{\text{simu}}$lation-$\underline{\text{free}}$ water$\underline{\text{mark}}$ing framework that circumvents this limitation by exploiting the inherent stability of image low-frequency components. We first systematically establish that low-frequency components exhibit significant robustness against a wide range of attacks. Building on this foundation, SimuFreeMark embeds watermarks directly into the deep feature space of the low-frequency components, leveraging a pre-trained variational autoencoder (VAE) to bind the watermark with structurally stable image representations. This design completely eliminates the need for noise simulation during training. Extensive experiments demonstrate that SimuFreeMark outperforms state-of-the-art methods across a wide range of conventional and semantic attacks, while maintaining superior visual quality.

翻译：人工智能生成内容（AIGC）的快速发展对鲁棒图像水印技术提出了迫切需求，要求其能够同时抵御传统信号处理和新型语义编辑攻击。当前基于深度学习的方法依赖于手工构建的噪声模拟层进行训练，这本质上限制了其对未知失真的泛化能力。本文提出$\textbf{SimuFreeMark}$，一种无需噪声$\underline{\text{模拟}}$的水$\underline{\text{印}}$框架，通过利用图像低频分量的固有稳定性来规避这一局限。我们首先系统性地论证了低频分量对广泛攻击具有显著鲁棒性。基于此，SimuFreeMark将水印直接嵌入低频分量的深度特征空间，利用预训练的变分自编码器（VAE）将水印与结构稳定的图像表示绑定。该设计完全消除了训练过程中对噪声模拟的需求。大量实验表明，SimuFreeMark在多种传统攻击和语义攻击中均优于现有先进方法，同时保持了卓越的视觉质量。