  Invisible watermarks safeguard images' copyright by embedding hidden messages
only detectable by owners. They also prevent people from misusing images,
especially those generated by AI models. We propose a family of regeneration
attacks to remove these invisible watermarks. The proposed attack method first
adds random noise to an image to destroy the watermark and then reconstructs
the image. This approach is flexible and can be instantiated with many existing
image-denoising algorithms and pre-trained generative models such as diffusion
models. Through formal proofs and empirical results, we show that all invisible
watermarks are vulnerable to the proposed attack. For a particularly resilient
watermark, RivaGAN, regeneration attacks remove 93-99% of the invisible
watermarks while the baseline attacks remove no more than 3%. However, if we do
not require the watermarked image to look the same as the original one,
watermarks that keep the image semantically similar can be an alternative
defense against our attack. Our finding underscores the need for a shift in
research/industry emphasis from invisible watermarks to semantically similar
ones. Code is available at https://github.com/XuandongZhao/WatermarkAttacker.
