让图像任意局部嵌入水印，还能精准定位并提取信息。
Watermark Anything with Localized Messages
- 用深度学习模型在图像局部嵌入不可见水印
- 可在小至10%面积的区域提取32位水印，误差小于1比特
- 适合处理拼接、修复等复杂场景，支持多水印并行
现有图像水印方法难以处理局部水印问题，限制了其在实际场景中的应用。本文提出Watermark Anything Model（WAM），通过联合训练低分辨率模型，并后处理提升不可感知性与多水印能力，实现对图像局部区域的隐蔽水印嵌入与提取。该模型可将输入图像分割为水印区与非水印区，从多个小区域中恢复出一个或多个隐藏消息。实验表明，即使在高分辨率图像上，WAM在不可感知性和抗修复、拼接攻击方面表现优异，且能准确识别拼接图像中的水印区域，从不超图像表面10%的小区域（最小256x256）中提取32位消息，错误低于1比特。代码与模型权重已开源。
原文摘要 · Abstract (English)
Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different sources or have been edited. We introduce a deep-learning model for localized image watermarking, dubbed the Watermark Anything Model (WAM). The WAM embedder imperceptibly modifies the input image, while the extractor segments the received image into watermarked and non-watermarked areas and recovers one or several hidden messages from the areas found to be watermarked. The models are jointly trained at low resolution and without perceptual constraints, then post-trained for imperceptibility and multiple watermarks. Experiments show that WAM is competitive with state-of-the art methods in terms of imperceptibility and robustness, especially against inpainting and splicing, even on high-resolution images. Moreover, it offers new capabilities: WAM can locate watermarked areas in spliced images and extract distinct 32-bit messages with less than 1 bit error from multiple small regions -- no larger than 10% of the image surface -- even for small 256x256 images. Training and inference code and model weights are available at https://github.com/facebookresearch/watermark-anything.
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