面向自回归图像生成的免训练水印方法
Training-Free Watermarking for Autoregressive Image Generation
AI总结：
针对自回归图像生成模型水印研究不足的问题，提出免训练水印框架IndexMark，基于码本冗余用匹配-替换法嵌入水印，在图像质量与验证精度上达SOTA，且对多种扰动鲁棒。
摘要：
不可见图像水印可保护图像所有权，防止视觉生成模型被恶意滥用。然而，现有生成式水印方法主要针对扩散模型设计，自回归图像生成模型的水印研究仍十分不足。我们提出IndexMark，一种适用于自回归图像生成模型的免训练水印框架。IndexMark受码本冗余特性启发：用相似索引替换自回归生成的索引，产生的视觉差异可忽略不计。IndexMark的核心组件是一种简单有效的“匹配-替换”方法，该方法基于令牌相似度从码本中精心挑选水印令牌，通过令牌替换提升水印令牌的使用占比，从而在不影响图像质量的前提下嵌入水印。水印验证通过计算生成图像中水印令牌的比例实现，Index Encoder（索引编码器）可进一步提升验证精度。此外，我们引入了一种辅助验证方案，以增强对裁剪攻击的鲁棒性。实验表明，IndexMark在图像质量和验证精度方面达到了当前最优性能，并且对多种扰动具有鲁棒性，包括裁剪、噪声、高斯模糊、随机擦除、颜色抖动和JPEG压缩。
英文摘要：
Invisible image watermarking can protect image ownership and prevent malicious misuse of visual generative models. However, existing generative watermarking methods are mainly designed for diffusion models while watermarking for autoregressive image generation models remains largely underexplored. We propose IndexMark, a training-free watermarking framework for autoregressive image generation models. IndexMark is inspired by the redundancy property of the codebook: replacing autoregressively generated indices with similar indices produces negligible visual differences. The core component in IndexMark is a simple yet effective match-then-replace method, which carefully selects watermark tokens from the codebook based on token similarity, and promotes the use of watermark tokens through token replacement, thereby embedding the watermark without affecting the image quality. Watermark verification is achieved by calculating the proportion of watermark tokens in generated images, with precision further improved by an Index Encoder. Furthermore, we introduce an auxiliary validation scheme to enhance robustness against cropping attacks. Experiments demonstrate that IndexMark achieves state-of-the-art performance in terms of image quality and verification accuracy, and exhibits robustness against various perturbations, including cropping, noises, Gaussian blur, random erasing, color jittering, and JPEG compression.
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