Fan, Lixin and Ng, Kam Woh and Chan, Chee Seng and Yang, Qiang (2022) DeepIPR: Deep neural network ownership verification with passports. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (10). pp. 6122-6139. ISSN 0162-8828, DOI https://doi.org/10.1109/TPAMI.2021.3088846.
Full text not available from this repository.Abstract
With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious threats to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN inference performance of an original task will be significantly deteriorated due to forged passports. In other words, genuine passports are not only verified by looking for the predefined signatures, but also reasserted by the unyielding DNN model inference performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code is available at https://github.com/kamwoh/DeepIPR.
| Item Type: | Article |
|---|---|
| Funders: | National Key Research and Development Program of China [2020YFB1805501], Fundamental Research Grant Scheme (FRGS) MoHE from the Ministry of Education Malaysia [FP021-2018A], NVIDIA Corporation |
| Uncontrolled Keywords: | Watermarking; Data models; Computational modeling; Analytical models; Training; Task analysis; Neural networks; Deep model protection; model ownership verification; intellectual property protection; model security; deep learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Sep 2023 04:11 |
| Last Modified: | 18 Sep 2023 04:11 |
| URI: | http://eprints.um.edu.my/id/eprint/41296 |
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