Watermarking Deep Neural Networks for Embedded Systems
Deep neural networks (DNNs) have become an important tool for bringing intelligence to mobile and embedded devices. The increasingly wide deployment, sharing and potential commercialization of DNN models create a compelling need for intellectual property (IP) protection. Recently, DNN watermarking e...
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| Vydáno v: | 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) s. 1 - 8 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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ACM
01.11.2018
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| Témata: | |
| ISSN: | 1558-2434 |
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| Abstract | Deep neural networks (DNNs) have become an important tool for bringing intelligence to mobile and embedded devices. The increasingly wide deployment, sharing and potential commercialization of DNN models create a compelling need for intellectual property (IP) protection. Recently, DNN watermarking emerges as a plausible IP protection method. Enabling DNN watermarking on embedded devices in a practical setting requires a black-box approach. Existing DNN watermarking frameworks either fail to meet the black-box requirement or are susceptible to several forms of attacks. We propose a watermarking framework by incorporating the author's signature in the process of training DNNs. While functioning normally in regular cases, the resulting watermarked DNN behaves in a different, predefined pattern when given any signed inputs, thus proving the authorship. We demonstrate an example implementation of the framework on popular image classification datasets and show that strong watermarks can be embedded in the models. |
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| AbstractList | Deep neural networks (DNNs) have become an important tool for bringing intelligence to mobile and embedded devices. The increasingly wide deployment, sharing and potential commercialization of DNN models create a compelling need for intellectual property (IP) protection. Recently, DNN watermarking emerges as a plausible IP protection method. Enabling DNN watermarking on embedded devices in a practical setting requires a black-box approach. Existing DNN watermarking frameworks either fail to meet the black-box requirement or are susceptible to several forms of attacks. We propose a watermarking framework by incorporating the author's signature in the process of training DNNs. While functioning normally in regular cases, the resulting watermarked DNN behaves in a different, predefined pattern when given any signed inputs, thus proving the authorship. We demonstrate an example implementation of the framework on popular image classification datasets and show that strong watermarks can be embedded in the models. |
| Author | Potkonjak, Miodrag Guo, Jia |
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| Snippet | Deep neural networks (DNNs) have become an important tool for bringing intelligence to mobile and embedded devices. The increasingly wide deployment, sharing... |
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| SubjectTerms | ACM Reference format Cloud computing deep neural networks Embedded systems Neural networks Software algorithms Watermarking |
| Title | Watermarking Deep Neural Networks for Embedded Systems |
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