Existing approaches for watermarking AI-generated images often rely on post-hoc methods applied in pixel space, introducing computational overhead and potential visual artifacts. In this work, we explore latent space watermarking and introduce DistSeal, a unified approach for latent watermarking that works across both diffusion and autoregressive models. Our approach works by training post-hoc watermarking models in the latent space of generative models. We demonstrate that these latent watermarkers can be effectively distilled either into the generative model itself or into the latent decoder, enabling in-model watermarking. The resulting latent watermarks achieve competitive robustness while offering similar imperceptibility and up to 20x speedup compared to pixel-space baselines. Our experiments further reveal that distilling latent watermarkers outperforms distilling pixel-space ones, providing a solution that is both more efficient and more robust.

翻译：现有针对AI生成图像的水印方法通常依赖于在像素空间应用的后处理技术，这会引入计算开销并可能产生视觉伪影。本研究探索了潜在空间水印技术，提出了DistSeal——一种适用于扩散模型与自回归模型的统一潜在空间水印方法。我们的方法通过在生成模型的潜在空间中训练后处理水印模型来实现。实验证明，这些潜在水印器能够被有效蒸馏至生成模型内部或潜在解码器中，从而实现模型内嵌式水印。所获得的潜在水印在保持竞争性鲁棒性的同时，具有与像素空间基线相当的不可感知性，且速度提升最高可达20倍。进一步实验表明，蒸馏潜在水印器的效果优于蒸馏像素空间水印器，这为同时提升效率与鲁棒性提供了解决方案。