Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914150129598464 |
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| author | Zhou, Zhenghao Li, Yiyan Yu, Xinjie Ping, Jian Xu, Xiaoyuan Yan, Zheng Shahidehpour, Mohammad |
| author_facet | Zhou, Zhenghao Li, Yiyan Yu, Xinjie Ping, Jian Xu, Xiaoyuan Yan, Zheng Shahidehpour, Mohammad |
| contents | Data has been regarded as a valuable asset with the fast development of artificial intelligence technologies. In this paper, we introduce deep-learning neural network-based frequency-domain watermarking for protecting energy system time series data assets and secure data authenticity when being shared or traded across communities. First, the concept and desired watermarking characteristics are introduced. Second, a deep-learning neural network-based watermarking model with specially designed loss functions and network structure is proposed to embed watermarks into the original dataset. Third, a frequency-domain data preprocessing method is proposed to eliminate the frequency bias of neural networks when learning time series datasets to enhance the model performances. Last, a comprehensive watermarking performance evaluation framework is designed for measuring its invisibility, restorability, robustness, secrecy, false-positive detection, generalization, and capacity. Case studies based on practical load and photovoltaic time series datasets demonstrate the effectiveness of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection Zhou, Zhenghao Li, Yiyan Yu, Xinjie Ping, Jian Xu, Xiaoyuan Yan, Zheng Shahidehpour, Mohammad Signal Processing Data has been regarded as a valuable asset with the fast development of artificial intelligence technologies. In this paper, we introduce deep-learning neural network-based frequency-domain watermarking for protecting energy system time series data assets and secure data authenticity when being shared or traded across communities. First, the concept and desired watermarking characteristics are introduced. Second, a deep-learning neural network-based watermarking model with specially designed loss functions and network structure is proposed to embed watermarks into the original dataset. Third, a frequency-domain data preprocessing method is proposed to eliminate the frequency bias of neural networks when learning time series datasets to enhance the model performances. Last, a comprehensive watermarking performance evaluation framework is designed for measuring its invisibility, restorability, robustness, secrecy, false-positive detection, generalization, and capacity. Case studies based on practical load and photovoltaic time series datasets demonstrate the effectiveness of the proposed method. |
| title | Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.07802 |