Watermark Anything with Localized Messages
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.
67331de6f6f2d658c70f15f5
Tom Sander
claimed_verified
2024-12-05T09:29:52.976Z
6548e467b17b77265dc334cb
/avatars/55f45f8ee6a4f477657cf0dd8409c533.svg
Tom Sander
user
tomsander1998
67331de6f6f2d658c70f15f6
Pierre Fernandez
claimed_verified
2025-01-20T09:30:51.093Z
616c22e040e2f69baa1c7ae0
/avatars/d6c00e7d98e5e8a52e99aa7b1a7815ee.svg
Pierre Fernandez
user
pierrefdz
67331de6f6f2d658c70f15f7
Alain Durmus
67331de6f6f2d658c70f15f8
Teddy Furon
67331de6f6f2d658c70f15f9
Matthijs Douze
Watermark Anything with Localized Messages
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.
The Watermark Anything Model (WAM) is a deep-learning approach for localized image watermarking, offering high imperceptibility and robustness, including capabilities to locate and extract distinct watermarks from small image regions.
deep-learning localized image watermarking Watermark Anything Model WAM imperceptibility robustness inpainting splicing high-resolution images watermarked areas distinct watermarks