Cargando…
Securing content-based image retrieval on the cloud using generative models
Content-based image retrieval (CBIR) with deep neural networks (DNNs) on the cloud has tremendous business and technical advantages to handle large-scale image repositories. However, cloud-based CBIR service raises challenges in image data and DNN model security. Typically, users who wish to request...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992788/ https://www.ncbi.nlm.nih.gov/pubmed/35431613 http://dx.doi.org/10.1007/s11042-022-12880-6 |
_version_ | 1784683800346755072 |
---|---|
author | Wang, Yong Wang, Fan-chuan Liu, Fei Wang, Xiao-hu |
author_facet | Wang, Yong Wang, Fan-chuan Liu, Fei Wang, Xiao-hu |
author_sort | Wang, Yong |
collection | PubMed |
description | Content-based image retrieval (CBIR) with deep neural networks (DNNs) on the cloud has tremendous business and technical advantages to handle large-scale image repositories. However, cloud-based CBIR service raises challenges in image data and DNN model security. Typically, users who wish to request CBIR services on the cloud require their input images remaining confidential. On the other hand, image owners may intentionally (or unintentionally) upload adversarial examples to the cloud servers, which potentially leads to the misbehavior of CBIR services. Generative Adversarial Networks (GANs) can be utilized to defense against such malicious behavior. However, the GANs model, if not well protected, can be easily abused by the cloud to reconstruct the users’ original image data. In this paper, we focus on the problem of secure generative model evaluation and secure gradient descent (GD) computation in GANs. We propose two secure generative model evaluation algorithms and two secure minimizer protocols. Furthermore, we propose and implement Sec-Defense-Gan, a secure image reconstruction framework which can keep the image data, the generative model details and corresponding outputs confidential from the cloud. Finally, We carried out a set of benchmarks over two public available image datasets to show the performance and correctness of Sec-Defense-Gan. |
format | Online Article Text |
id | pubmed-8992788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-89927882022-04-11 Securing content-based image retrieval on the cloud using generative models Wang, Yong Wang, Fan-chuan Liu, Fei Wang, Xiao-hu Multimed Tools Appl Article Content-based image retrieval (CBIR) with deep neural networks (DNNs) on the cloud has tremendous business and technical advantages to handle large-scale image repositories. However, cloud-based CBIR service raises challenges in image data and DNN model security. Typically, users who wish to request CBIR services on the cloud require their input images remaining confidential. On the other hand, image owners may intentionally (or unintentionally) upload adversarial examples to the cloud servers, which potentially leads to the misbehavior of CBIR services. Generative Adversarial Networks (GANs) can be utilized to defense against such malicious behavior. However, the GANs model, if not well protected, can be easily abused by the cloud to reconstruct the users’ original image data. In this paper, we focus on the problem of secure generative model evaluation and secure gradient descent (GD) computation in GANs. We propose two secure generative model evaluation algorithms and two secure minimizer protocols. Furthermore, we propose and implement Sec-Defense-Gan, a secure image reconstruction framework which can keep the image data, the generative model details and corresponding outputs confidential from the cloud. Finally, We carried out a set of benchmarks over two public available image datasets to show the performance and correctness of Sec-Defense-Gan. Springer US 2022-04-08 2022 /pmc/articles/PMC8992788/ /pubmed/35431613 http://dx.doi.org/10.1007/s11042-022-12880-6 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Wang, Yong Wang, Fan-chuan Liu, Fei Wang, Xiao-hu Securing content-based image retrieval on the cloud using generative models |
title | Securing content-based image retrieval on the cloud using generative models |
title_full | Securing content-based image retrieval on the cloud using generative models |
title_fullStr | Securing content-based image retrieval on the cloud using generative models |
title_full_unstemmed | Securing content-based image retrieval on the cloud using generative models |
title_short | Securing content-based image retrieval on the cloud using generative models |
title_sort | securing content-based image retrieval on the cloud using generative models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8992788/ https://www.ncbi.nlm.nih.gov/pubmed/35431613 http://dx.doi.org/10.1007/s11042-022-12880-6 |
work_keys_str_mv | AT wangyong securingcontentbasedimageretrievalonthecloudusinggenerativemodels AT wangfanchuan securingcontentbasedimageretrievalonthecloudusinggenerativemodels AT liufei securingcontentbasedimageretrievalonthecloudusinggenerativemodels AT wangxiaohu securingcontentbasedimageretrievalonthecloudusinggenerativemodels |