Demystify scrambled and encoded images with auto-encoders
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2022 IEEE International Conference on Big Data |
| Editors | Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan |
| Publisher | IEEE |
| Pages | 6460-6465 |
| ISBN (Electronic) | 9781665480451 |
| ISBN (Print) | 978-1-6654-8046-8 |
| Publication status | Published - Dec 2022 |
Publication series
| Name | Proceedings - IEEE International Conference on Big Data, Big Data |
|---|
Conference
| Title | 2022 IEEE International Conference on Big Data (IEEE BigData 2022) |
|---|---|
| Location | Osaka International Convention Center |
| Place | Japan |
| City | Osaka |
| Period | 17 - 20 December 2022 |
Link(s)
Abstract
Monitoring systems have become more and more popular. A typical example is a smart home monitoring system which provides peace of mind for people staying far away from their homes. This system can be deployed for different purposes, such as to take care elderly staying at home alone or to provide anti-theft when nobody is at home. In this type of monitoring systems, there is always a problem with protecting the privacy and security of captured images. This problem is often addressed by either scrambling or encoding techniques that add noise in or to distort the images. In the IEEE Big Data 2022 Cup, the challenge is to detect and perform matching between an original image and its scrambled or encoded version. Generally, if we are able to train a machine learning model that would correctly identify the matching, it will be proof that the applied scrambling or encoding technique is not strong enough to protect data and hence it needs to be strengthened further. In this paper, we present an auto-encoder architecture that allows us to demystify scrambled or encoded images as long as the scrambling or encoding techniques are deterministic. Source code is publicly available1. © 2022 IEEE.
Research Area(s)
- auto-encoder, encoding, image matching, Privacy, scrambling, security
Citation Format(s)
Demystify scrambled and encoded images with auto-encoders. / Nguyen, Huu-Thanh; Hieu Vu, Quang; Huynh, Anh-Dung.
Proceedings - 2022 IEEE International Conference on Big Data. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. IEEE, 2022. p. 6460-6465 (Proceedings - IEEE International Conference on Big Data, Big Data).Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
