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Toward Secure Image Denoising: A Machine Learning Based Realization
Zheng Y.1; Wang C.1; Zhou J.2
2018-09-10
Conference NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
Pages6936-6940
Conference DateAPR 15-20, 2018
Conference PlaceCalgary, 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.

KeywordCloud Computing Image Denoising Machine Learning Neural Network Privacy
DOI10.1109/ICASSP.2018.8462073
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000446384607019
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Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.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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