Privacy mechanism for photo storage

Photo-sharing has become an integral part of our daily lives. We willingly share photos online to pass on information, share about our lives and take part in online marketing activities. There is also a new trend of businesses engaging in digital marketing, by entailing its consumers to take photos...

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Main Author: Heng, Wei Shan
Other Authors: Tay Wee Peng
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/74780
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-747802023-07-07T16:17:23Z Privacy mechanism for photo storage Heng, Wei Shan Tay Wee Peng School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Photo-sharing has become an integral part of our daily lives. We willingly share photos online to pass on information, share about our lives and take part in online marketing activities. There is also a new trend of businesses engaging in digital marketing, by entailing its consumers to take photos of themselves with their products, in exchange for giveaway prizes. Furthermore, on Instagram alone, up to 95 million photos and videos are uploaded each day [24]. With this vast number of photos being shared daily on so many different platforms, one must consider the privacy aspect of such a phenomenon, especially since there is a tendency to neglect filtering our photos before uploading them on these platforms. This work aims to provide an alternative image privacy mechanism by first detecting object classes in an image, followed by applying a blurring filter onto privacy-sensitive object classes. Deep learning framework is applied for image segmentation on 21 object classes and image processing techniques are used for blurring the object classes. Bachelor of Engineering 2018-05-24T02:08:49Z 2018-05-24T02:08:49Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74780 en Nanyang Technological University 44 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Heng, Wei Shan
Privacy mechanism for photo storage
description Photo-sharing has become an integral part of our daily lives. We willingly share photos online to pass on information, share about our lives and take part in online marketing activities. There is also a new trend of businesses engaging in digital marketing, by entailing its consumers to take photos of themselves with their products, in exchange for giveaway prizes. Furthermore, on Instagram alone, up to 95 million photos and videos are uploaded each day [24]. With this vast number of photos being shared daily on so many different platforms, one must consider the privacy aspect of such a phenomenon, especially since there is a tendency to neglect filtering our photos before uploading them on these platforms. This work aims to provide an alternative image privacy mechanism by first detecting object classes in an image, followed by applying a blurring filter onto privacy-sensitive object classes. Deep learning framework is applied for image segmentation on 21 object classes and image processing techniques are used for blurring the object classes.
author2 Tay Wee Peng
author_facet Tay Wee Peng
Heng, Wei Shan
format Final Year Project
author Heng, Wei Shan
author_sort Heng, Wei Shan
title Privacy mechanism for photo storage
title_short Privacy mechanism for photo storage
title_full Privacy mechanism for photo storage
title_fullStr Privacy mechanism for photo storage
title_full_unstemmed Privacy mechanism for photo storage
title_sort privacy mechanism for photo storage
publishDate 2018
url http://hdl.handle.net/10356/74780
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