MetaFinger: Fingerprinting the Deep Neural Networks with Meta-training
Kang Yang, Run Wang, Lina Wang
Abstract
As deep neural networks (DNNs) play a critical role in various fields, the models themselves hence are becoming an important asset that needs to be protected. To achieve this, various neural network fingerprint methods have been proposed. However, existing fingerprint methods fingerprint the decision boundary by adversarial examples, which is not robust to model modification and adversarial defenses. To fill this gap, we propose a robust fingerprint method MetaFinger, which fingerprints the inner decision area of the model by meta-training, rather than the decision boundary. Specifically, we first generate many shadow models with DNN augmentation as meta-data. Then we optimize some images by meta-training to ensure that only models derived from the protected model can recognize them. To demonstrate the robustness of our fingerprint approach, we evaluate our method against two types of attacks including input modification and model modification. Experiments show that our method achieves 99.34% and 97.69% query accuracy on average, surpassing existing methods over 30%, 25% on CIFAR-10 and Tiny-ImageNet, respectively. Our code is available at https://github.com/kangyangWHU/MetaFinger.