Toward Secure Image Denoising: A Machine Learning Based Realization | |
Zheng Y.1; Wang C.1; Zhou J.2![]() | |
2018-09-10 | |
Conference Name | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Source Publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
Volume | 2018-April |
Pages | 6936-6940 |
Conference Date | APR 15-20, 2018 |
Conference Place | Calgary, CANADA |
Abstract | Image denoising via machine learning techniques, particularly neural networks, has been shown to achieve state-of-the-art performance. However, in practice security and privacy issues undesirably arise in applying a trained machine learning model to image denoising. In this paper, we propose a system framework that enables the owner of a trained machine learning model to provide secure image denoising service to an authorized user, via the aid of cloud computing. Our framework ensures that the cloud server learns nothing about the model and the user's images, while the user learns nothing about the model except denoised images. Experiments are conducted for performance evaluation, and the results show that our design can achieve denoising quality close to that in the plaintext domain. For future work, we plan to explore various directions for optimizing the runtime performance. |
Keyword | Cloud Computing Image Denoising Machine Learning Neural Network Privacy |
DOI | 10.1109/ICASSP.2018.8462073 |
URL | View the original |
Indexed By | SCI |
Language | 英语 |
WOS Research Area | Acoustics ; Engineering |
WOS Subject | Acoustics ; Engineering, Electrical & Electronic |
WOS ID | WOS:000446384607019 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Affiliation | 1.City University of Hong Kong 2.Universidade de Macau |
Recommended Citation GB/T 7714 | Zheng Y.,Wang C.,Zhou J.. Toward Secure Image Denoising: A Machine Learning Based Realization[C],2018:6936-6940. |
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