title: Watermark Anything with Localized Messages
summary: 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.
paper_authors._id: 67331de6f6f2d658c70f15f5
paper_authors.name: Tom Sander
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-05T09:29:52.976Z
paper_authors._id: 6548e467b17b77265dc334cb
paper_authors.avatarUrl: /avatars/55f45f8ee6a4f477657cf0dd8409c533.svg
paper_authors.fullname: Tom Sander
paper_authors.type: user
paper_authors.user: tomsander1998
paper_authors._id: 67331de6f6f2d658c70f15f6
paper_authors.name: Pierre Fernandez
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T09:30:51.093Z
paper_authors._id: 616c22e040e2f69baa1c7ae0
paper_authors.avatarUrl: /avatars/d6c00e7d98e5e8a52e99aa7b1a7815ee.svg
paper_authors.fullname: Pierre Fernandez
paper_authors.type: user
paper_authors.user: pierrefdz
paper_authors._id: 67331de6f6f2d658c70f15f7
paper_authors.name: Alain Durmus
paper_authors._id: 67331de6f6f2d658c70f15f8
paper_authors.name: Teddy Furon
paper_authors._id: 67331de6f6f2d658c70f15f9
paper_authors.name: Matthijs Douze
paper_title: Watermark Anything with Localized Messages
paper_summary: 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.
paper_ai_keywords: deep-learning model localized image watermarking Watermark Anything Model (WAM) embedder extractor segmentation hidden messages joint training low resolution perceptual constraints post-trained imperceptibility robustness inpainting splicing locate watermarked areas 32-bit messages bit error