  Watermarking has become one of promising techniques to not only aid in
identifying AI-generated images but also serve as a deterrent against the
unethical use of these models. However, the robustness of watermarking
techniques has not been extensively studied recently. In this paper, we
investigate the robustness of generative watermarking, which is created from
the integration of watermarking embedding and text-to-image generation
processing in generative models, e.g., latent diffusion models. Specifically,
we propose three attacking methods, i.e., discriminator-based attacks, edge
prediction-based attacks, and fine-tune-based attacks, under the scenario where
the watermark decoder is not accessible. The model is allowed to be fine-tuned
to created AI agents with specific generative tasks for personalizing or
specializing. We found that generative watermarking methods are robust to
direct evasion attacks, like discriminator-based attacks, or manipulation based
on the edge information in edge prediction-based attacks but vulnerable to
malicious fine-tuning. Experimental results show that our fine-tune-based
attacks can decrease the accuracy of the watermark detection to nearly
$67.92\%$. In addition, We conduct an ablation study on the length of
fine-tuned messages, encoder/decoder's depth and structure to identify key
factors that impact the performance of fine-tune-based attacks.
