| Title | Protecting the Intellectual Property of Deep Neural Networks with Watermarking: The Frequency Domain Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Meng, Zhong, Qi, Zhang, Leo Yu, Du, Yajuan, Zhang, Jun, Xiang, Yong |
| Conference Name | 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
| Date Published | dec |
| Keywords | composability, deep neural networks, frequency transform, frequency-domain analysis, image processing, intellectual property, intellectual property security, Measurement, Neural networks, policy-based governance, pubcrawl, Resiliency, Signal processing algorithms, Watermarking |
| Abstract | Similar to other digital assets, deep neural network (DNN) models could suffer from piracy threat initiated by insider and/or outsider adversaries due to their inherent commercial value. DNN watermarking is a promising technique to mitigate this threat to intellectual property. This work focuses on black-box DNN watermarking, with which an owner can only verify his ownership by issuing special trigger queries to a remote suspicious model. However, informed attackers, who are aware of the watermark and somehow obtain the triggers, could forge fake triggers to claim their ownerships since the poor robustness of triggers and the lack of correlation between the model and the owner identity. This consideration calls for new watermarking methods that can achieve better trade-off for addressing the discrepancy. In this paper, we exploit frequency domain image watermarking to generate triggers and build our DNN watermarking algorithm accordingly. Since watermarking in the frequency domain is high concealment and robust to signal processing operation, the proposed algorithm is superior to existing schemes in resisting fraudulent claim attack. Besides, extensive experimental results on 3 datasets and 8 neural networks demonstrate that the proposed DNN watermarking algorithm achieves similar performance on functionality metrics and better performance on security metrics when compared with existing algorithms. |
| DOI | 10.1109/TrustCom50675.2020.00062 |
| Citation Key | li_protecting_2020 |
Protecting the Intellectual Property of Deep Neural Networks with Watermarking: The Frequency Domain Approach