title: Hidden in the Noise: Two-Stage Robust Watermarking for Images
summary: As the quality of image generators continues to improve, deepfakes become a
  topic of considerable societal debate. Image watermarking allows responsible
  model owners to detect and label their AI-generated content, which can mitigate
  the harm. Yet, current state-of-the-art methods in image watermarking remain
  vulnerable to forgery and removal attacks. This vulnerability occurs in part
  because watermarks distort the distribution of generated images,
  unintentionally revealing information about the watermarking techniques.
    In this work, we first demonstrate a distortion-free watermarking method for
  images, based on a diffusion model's initial noise. However, detecting the
  watermark requires comparing the initial noise reconstructed for an image to
  all previously used initial noises. To mitigate these issues, we propose a
  two-stage watermarking framework for efficient detection. During generation, we
  augment the initial noise with generated Fourier patterns to embed information
  about the group of initial noises we used. For detection, we (i) retrieve the
  relevant group of noises, and (ii) search within the given group for an initial
  noise that might match our image. This watermarking approach achieves
  state-of-the-art robustness to forgery and removal against a large battery of
  attacks.
paper_authors._id: 67584aed66fd404ad4478569
paper_authors.name: Kasra Arabi
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paper_authors.fullname: Kasra Arabi
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paper_authors.user: kasraarabi
paper_authors._id: 67584aed66fd404ad447856a
paper_authors.name: Benjamin Feuer
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paper_authors.fullname: Benjamin Feuer
paper_authors.type: user
paper_authors.user: penfever
paper_authors._id: 67584aed66fd404ad447856b
paper_authors.name: R. Teal Witter
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paper_authors.fullname: R. Teal Witter
paper_authors.type: user
paper_authors.user: rtealwitter
paper_authors._id: 67584aed66fd404ad447856c
paper_authors.name: Chinmay Hegde
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T10:15:48.130Z
paper_authors._id: 631620f6894404e25068856f
paper_authors.avatarUrl: /avatars/52c30caa0ee11347f82420a14ec19996.svg
paper_authors.fullname: Chinmay Hegde
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paper_authors.user: chegde
paper_authors._id: 67584aed66fd404ad447856d
paper_authors.name: Niv Cohen
paper_title: Hidden in the Noise: Two-Stage Robust Watermarking for Images
paper_summary: As the quality of image generators continues to improve, deepfakes become a
  topic of considerable societal debate. Image watermarking allows responsible
  model owners to detect and label their AI-generated content, which can mitigate
  the harm. Yet, current state-of-the-art methods in image watermarking remain
  vulnerable to forgery and removal attacks. This vulnerability occurs in part
  because watermarks distort the distribution of generated images,
  unintentionally revealing information about the watermarking techniques.
    In this work, we first demonstrate a distortion-free watermarking method for
  images, based on a diffusion model's initial noise. However, detecting the
  watermark requires comparing the initial noise reconstructed for an image to
  all previously used initial noises. To mitigate these issues, we propose a
  two-stage watermarking framework for efficient detection. During generation, we
  augment the initial noise with generated Fourier patterns to embed information
  about the group of initial noises we used. For detection, we (i) retrieve the
  relevant group of noises, and (ii) search within the given group for an initial
  noise that might match our image. This watermarking approach achieves
  state-of-the-art robustness to forgery and removal against a large battery of
  attacks.
paper_ai_summary: The proposed two-stage watermarking framework using Fourier patterns and diffusion model's initial noise enhances robustness to forgery and removal in AI-generated images.
paper_ai_keywords: diffusion model initial noise Fourier patterns watermarking forgery removal attacks