Hidden in the Noise: Two-Stage Robust Watermarking for Images
Authors: name: Kasra Arabi, name: Benjamin Feuer, name: R. Teal Witter, name: Chinmay Hegde, name: Niv Cohen

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.