Dimensions overall score 9.0

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• DistSeal introduces a unified approach for latent watermarking that works across both diffusion and autoregressive models.method 95%
• Latent watermarks achieve up to 20x speedup compared to pixel-space baselines.result 90%
• Distilling latent watermarkers outperforms distilling pixel-space ones in terms of efficiency and robustness.result 90%
• Latent watermarking can be effectively distilled either into the generative model itself or into the latent decoder, enabling in-model watermarking.technical 85%
• Existing pixel-space watermarking methods introduce computational overhead and potential visual artifacts.limitation 80%
• DistSeal's latent watermarks achieve competitive robustness while offering similar imperceptibility to pixel-space methods.result 85%
• The approach works by training post-hoc watermarking models in the latent space of generative models.method 90%
• Watermarks can be removed with enough effort, indicating a security limitation.limitation 80%

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