DeepIPR: Deep Neural Network Ownership Verification With Passports

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...

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Vydané v:IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 10; s. 6122 - 6139
Hlavní autori: Fan, Lixin, Ng, Kam Woh, Chan, Chee Seng, Yang, Qiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Ambiguity
Analytical models
Artificial neural networks
Computational modeling
Counterfeit
Data models
deep learning
Deep model protection
Forging
Inference
intellectual property protection
Inventions
model ownership verification
model security
Neural networks
Ownership
Passports
Passports & visas
Task analysis
Training
Verification
Watermarking
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Popis
Shrnutí: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 .
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3088846