Information-Theoretic Framework for Black-Box Model Watermarking
A recent paper published on arXiv introduces an information-theoretic approach to tackle the capacity crisis in black-box model watermarking. Cited as arXiv:2409.06130v2, the research emphasizes the balance between robustness and predictive utility in current watermarking techniques. The authors examine this challenge through an information-theoretic lens, revealing a critical capacity issue: using only predicted labels does not provide enough capacity to embed strong ownership signals without compromising accuracy. To address this, they suggest a framework that utilizes the top-k output, enhancing the effective capacity for watermarking while maintaining predictive accuracy. Comprehensive experiments in image, text, and tabular domains validate the method's efficacy, highlighting its importance for safeguarding machine learning models as intellectual property in the AI sector.
Key facts
- Paper arXiv:2409.06130v2 proposes a new black-box ownership verification framework.
- The framework uses top-k output to increase watermarking capacity.
- Existing methods suffer from a trade-off between robustness and predictive utility.
- The authors identify a fundamental capacity crisis in relying solely on predicted labels.
- Experiments were conducted across image, text, and tabular domains.
- The method preserves predictive performance while embedding robust ownership signals.
- The paper is available on arXiv at https://arxiv.org/abs/2409.06130.
- The research addresses the need to protect machine learning models as intellectual property.
Entities
Institutions
- arXiv