TokenMark: A Modality-Agnostic Watermark for Pre-trained Transformers
This addresses the need for versatile watermarking to protect model ownership across different data modalities and tasks, representing a novel method for a known bottleneck.
The paper tackled the problem of model ownership verification by proposing TokenMark, a modality-gnostic watermarking system for pre-trained Transformers, which significantly improved robustness, efficiency, and universality in experiments.
Watermarking is a critical tool for model ownership verification. However, existing watermarking techniques are often designed for specific data modalities and downstream tasks, without considering the inherent architectural properties of the model. This lack of generality and robustness underscores the need for a more versatile watermarking approach. In this work, we investigate the properties of Transformer models and propose TokenMark, a modality-agnostic, robust watermarking system for pre-trained models, leveraging the permutation equivariance property. TokenMark embeds the watermark by fine-tuning the pre-trained model on a set of specifically permuted data samples, resulting in a watermarked model that contains two distinct sets of weights -- one for normal functionality and the other for watermark extraction, the latter triggered only by permuted inputs. Extensive experiments on state-of-the-art pre-trained models demonstrate that TokenMark significantly improves the robustness, efficiency, and universality of model watermarking, highlighting its potential as a unified watermarking solution.