\section{Conclusion}
\label{sec:conclusion}

In this paper, we introduce {\name}, a new copyright protection paradigm in the AI era. 
Unlike existing attack-based approaches, {\name} requires no domain knowledge of misuse scenarios or mechanisms.
{\name} bridges inverse problems and copyright protection and formulates learning-based \textit{hard-to-remove} visible watermarking as a bi-level optimization problem. 
Built upon recent optimality guarantees for inverse problems, 
we propose a new meta-learning solution for {\name}. 
We validate the effectiveness of our algorithm across watermarking scenarios.
Encouraged by the promising results, we identify two exciting directions for future work.
First, we will explore a training-free version of Harvim that embeds open-ended text watermarks, leveraging recent advances in image editing to enhance real-time efficiency.
Second, we will investigate the theoretical guarantees of the Harvim framework from two perspectives:
(1) determining the minimum distortion (masking) required to ensure a watermark is provably unremovable, and
(2) characterizing the maximum tolerable noise or perturbation under which a personalized concept can still be provably learned using DreamBooth.


% we plan to explore open-class watermarking to enhance {\name}'s scalability in the future.



% We introduce {\name}, a new copyright protection paradigm for the AI era.
% Unlike existing attack-based approaches, {\name} does not require domain knowledge of misuse scenarios or mechanisms.
% By bridging inverse problems and copyright protection, {\name} formulates learning-based \textit{hard-to-remove} visible watermarking as a bi-level optimization problem.
% Leveraging recent optimality guarantees for inverse problems, we propose a new meta-learning solution for {\name}.
% We evaluate our algorithm’s effectiveness across various image watermarking scenarios.
% Encouraged by these promising results, we plan to explore open-class watermarking to further enhance {\name}’s scalability in future work.