Hidden in the Noise: Two-Stage Robust Watermarking for Images
Kasra Arabi, Benjamin Feuer, R. Teal Witter, Chinmay Hegde, Niv Cohen
2024-12-11
Summary
This paper talks about a new method for watermarking images that makes it harder for others to remove or forge the watermark, helping to identify AI-generated content and reduce the risks of deepfakes.
What's the problem?
As AI-generated images become more realistic, it becomes important to label and identify them to prevent misuse, like spreading misinformation or creating fake images. However, existing watermarking methods are often vulnerable to attacks that can remove or alter the watermark, making it easy for someone to claim an AI-generated image as their own.
What's the solution?
The authors propose a two-stage watermarking system that first embeds a watermark into the image using a method that doesn't distort the image quality. They use a diffusion model's initial noise to create this watermark. To improve detection, they add Fourier patterns to the noise during image generation, which helps identify the watermark later. This approach allows for effective detection even if someone tries to tamper with the image, achieving better resistance against forgery and removal attacks.
Why it matters?
This research is important because it enhances the ability to track and verify AI-generated content, which is crucial in today's digital world where misinformation can spread quickly. By improving watermarking techniques, this work helps protect creators' rights and promotes trust in digital media, making it harder for malicious actors to misuse AI technology.
Abstract
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.