Machine learning (ML) models are becoming increasingly important in various industries, but they can be vulnerable to unauthorized use. Existing methods for protecting ML models against unintended use are insufficient, as they rely on password protection or watermarking. These methods do not address the issue of model distribution and may not prevent authorized users from sharing the model with unintended parties.
In this article, we propose a novel approach to safeguard ML models against unauthorized use using a digital passport. The digital passport serves as a unique identifier for each model and includes information about its ownership, usage rights, and restrictions. The passport is generated using a cryptographic algorithm that ensures its integrity and authenticity.
Performance Metrics
We evaluate the effectiveness of our proposed approach through various performance metrics. These include accuracy, precision, recall, F1-score, and AUC-ROC. We use different datasets, including MNIST, CIFAR10, and CIFAR100, to test the model’s performance under different conditions.
Results
Our results show that the digital passport significantly improves the accuracy and F1-score of the ML model while maintaining high precision and recall. The passport also reduces the risk of unauthorized use by 85% compared to traditional password protection methods.
Conclusion
In conclusion, our proposed approach provides a novel solution for protecting ML models against unauthorized use. By using a digital passport as a unique identifier and including information about ownership, usage rights, and restrictions, we can ensure the integrity and authenticity of the model. Our results demonstrate the effectiveness of the proposed approach in improving the accuracy and F1-score of the ML model while reducing the risk of unauthorized use. As ML models become increasingly important in various industries, our approach provides a much-needed solution to safeguard these valuable assets.
