Invisible watermarks safeguard images' copyrights by embedding hidden
messages detectable by owners. It also prevents people from misusing images,
especially those generated by AI models. Malicious adversaries can violate
these rights by removing the watermarks. In order to remove watermarks without
damaging the visual quality, the adversary needs to erase them while retaining
the essential information in the image. This is analogous to the encoding and
decoding process of generative autoencoders, especially variational
autoencoders (VAEs) and diffusion models. We propose a framework using
generative autoencoders to remove invisible watermarks and test it using VAEs
and diffusions. Our results reveal that, even without specific training,
off-the-shelf Stable Diffusion effectively removes most watermarks, surpassing
all current attackers. The result underscores the vulnerabilities in existing
watermarking schemes and calls for more robust methods for copyright
protection