Watermark Anything with Localized Messages
Authors: name: Tom Sander, name: Pierre Fernandez, name: Alain Durmus, name: Teddy Furon, name: Matthijs Douze

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 256times
256 images.