Hidden in the Noise: Two-Stage Robust Watermarking for Images
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.
67584aed66fd404ad4478569
Kasra Arabi
claimed_verified
2024-12-10T15:15:20.150Z
656a1590801ed9952f407547
/avatars/de90853b933511ab63b4b81fc8d9e1c0.svg
Kasra Arabi
user
kasraarabi
67584aed66fd404ad447856a
Benjamin Feuer
admin_assigned
2024-12-11T10:15:18.300Z
62f7f4efe7c1c9bf10c81465
https://cdn-avatars.huggingface.co/v1/production/uploads/62f7f4efe7c1c9bf10c81465/AYlOg0fkP1o4GAP-8Y3xt.jpeg
Benjamin Feuer
user
penfever
67584aed66fd404ad447856b
R. Teal Witter
admin_assigned
2024-12-11T10:15:23.537Z
637ff9cb3b36a53c22ed4c86
/avatars/3df0253a1127a5c5664850b0da09af2a.svg
R. Teal Witter
user
rtealwitter
67584aed66fd404ad447856c
Chinmay Hegde
admin_assigned
2024-12-11T10:15:48.130Z
631620f6894404e25068856f
/avatars/52c30caa0ee11347f82420a14ec19996.svg
Chinmay Hegde
user
chegde
67584aed66fd404ad447856d
Niv Cohen
Hidden in the Noise: Two-Stage Robust Watermarking for Images
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.
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.
diffusion model initial noise Fourier patterns watermarking forgery removal attacks