Effects of incremental training on watermarked neural networks
Deep learning has achieved extraordinary results in many different areas, ranging from autonomous driving [1], medical devices [2] to speech recognition and natural language processing [3]. Generating a high-performance neural network is costly in aspects of time, computational resources, and exp...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2023
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/167143 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Deep learning has achieved extraordinary results in many different areas, ranging from autonomous
driving [1], medical devices [2] to speech recognition and natural language processing
[3]. Generating a high-performance neural network is costly in aspects of time, computational resources,
and expertise, making the models valuable intellectual property (IP). As a result, there has
been a notable growth in attention and investments in the paradigm of machine learning. In recent
years, watermarking methods have been developed in order to protect the Intellectual Property
Rights (IPR) of neural networks, and many schemes have successfully prevented adversaries from
stealing such models. However, little has been studied on how Incremental Training would affect
the persistence of watermarks in such watermarking schemes. This investigation aims to discover
the effects of Incremental Training on in existing watermarking schemes.
Keywords: Intellectual Property Rights (IPR), Watermarking, Incremental Training |
|---|