多比特图像水印的自适应离散化方法

摘要

随着生成模型能够快速创建高保真图像，社会对错误信息和真实性的担忧日益加剧。一种有效的解决方案是多比特图像水印技术，该方法将多比特信息嵌入图像，使得验证者能够检测图像是否由特定生成器创建，并通过解码嵌入信息进一步追溯来源。现有方法在容量、对常见图像失真的鲁棒性及理论依据方面存在不足。为解决这些局限，我们提出ADD（加性、点积、解码）水印方法，其包含两个阶段：学习与多比特信息线性组合的水印并叠加至图像，以及通过水印图像与习得水印的内积进行解码。在标准MS-COCO基准测试中，针对48比特水印这一挑战性任务，ADD实现了100%的解码准确率，在多种图像失真条件下性能下降最多不超过2%，远低于现有最优方法14%的平均降幅。此外，ADD显著提升了计算效率，嵌入速度比现有最快方法提升2倍，解码速度提升7.4倍。我们还通过理论分析揭示了习得水印及其对应解码规则的有效性机制。

English

As generative models enable rapid creation of high-fidelity images, societal concerns about misinformation and authenticity have intensified. A promising remedy is multi-bit image watermarking, which embeds a multi-bit message into an image so that a verifier can later detect whether the image is generated by someone and further identify the source by decoding the embedded message. Existing approaches often fall short in capacity, resilience to common image distortions, and theoretical justification. To address these limitations, we propose ADD (Add, Dot, Decode), a multi-bit image watermarking method with two stages: learning a watermark to be linearly combined with the multi-bit message and added to the image, and decoding through inner products between the watermarked image and the learned watermark. On the standard MS-COCO benchmark, we demonstrate that for the challenging task of 48-bit watermarking, ADD achieves 100\% decoding accuracy, with performance dropping by at most 2\% under a wide range of image distortions, substantially smaller than the 14\% average drop of state-of-the-art methods. In addition, ADD achieves substantial computational gains, with 2-fold faster embedding and 7.4-fold faster decoding than the fastest existing method. We further provide a theoretical analysis explaining why the learned watermark and the corresponding decoding rule are effective.

多比特图像水印的自适应离散化方法

ADD for Multi-Bit Image Watermarking

摘要

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