Purification-Agnostic Proxy Learning for Agentic Copyright Watermarking against Adversarial Evidence Forgery
<|reference_start|>Purification-Agnostic Proxy Learning for Agentic Copyright Watermarking against Adversarial Evidence Forgery: With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorized use and illegal distribution of these models pose serious threats to intellectual property, necessitating effective copyright protection measures. Model watermarking has emerged as a key technique to address this issue, embedding ownership information within models to assert rightful ownership during copyright disputes. This paper presents several contributions to model watermarking: a self-authenticating black-box watermarking protocol using hash techniques, a study on evidence forgery attacks using adversarial perturbations, a proposed defense involving a purification step to counter adversarial attacks, and a purification-agnostic proxy learning method to enhance watermark reliability and model performance. Experimental results demonstrate the effectiveness of these approaches in improving the security, reliability, and performance of watermarked models.<|reference_end|>
@article{bao2024purification-agnostic,
    title={Purification-Agnostic Proxy Learning for Agentic Copyright Watermarking
  against Adversarial Evidence Forgery},
    author={Erjin Bao and Ching-Chun Chang and Hanrui Wang and Isao Echizen},
    journal={arXiv preprint arXiv:2409.01541},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01541},
    primaryClass={cs.CV cs.CR}
}