Breaking the Boundary Barrier: Robust Model Fingerprinting via Unlearnable Examples in Model-Parameter Space

Deep learning models represent valuable intellectual property due to their high development costs. To protect model ownership, existing fingerprinting techniques have been proposed to use adversarial examples to fingerprint a model's decision boundaries. However, these fingerprints are inherently fragile, as model decision boundaries are highly sensitive to common model modifications such as fine-tuning, pruning, and adversarial training. In this paper, we propose MFUE (Model Fingerprinting via Unlearnable Examples), a novel fingerprinting methodology that leverages the stable unlearnability of unlearnable examples to fingerprint arbitrary modified models in parameter space, fundamentally circumventing the inherent vulnerability of decision boundaries. To achieve robust model fingerprinting in parameter space, we are the first to identify that unlearnable examples, owing to their persistent training resistance, can serve as stable fingerprints beyond the model's decision boundaries. To endow unlearnable examples with robustness against arbitrary model modifications, we introduce adversarial training that simulates the randomness of model modifications by jointly optimizing the unlearnable examples over models at different training stages. We evaluate the performance of MFUE against six different attack types, including both model and input tampering. Through extensive experiments, we demonstrate that MFUE outperforms four existing methods in terms of robustness and uniqueness. The code of MFUE is publicly available at: https://github.com/SPHelixLab/MFUE/.