Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2024

Abstract

The use of social media has made it easy to communicate and share information over the internet. However, it also brings issues such as data privacy leakage, which can be exploited by recipients with malicious intentions to harm the sender. In this paper, we propose a deep neural network that analyzes user’s image for privacy sensitive content and automatically locates sensitive regions for obfuscation. Our approach relies solely on image level annotations and learns to (a) predict an overall privacy score, (b) detect sensitive attributes and (c) demarcate the sensitive regions for obfuscation, in a given input image. We validated the performance of our proposed method on three large datasets, VISPR, PASCAL VOC 2012 and MS COCO 2014, in terms of privacy score, attribute prediction and obfuscation performance. On the VISPR dataset, we achieved a Pearson correlation of 0.88 and a Spearman correlation of 0.86, outperforming previous methods. On PASCAL VOC 2012 and MS COCO 2014, our model achieved a mean IOU of 71.5% and 43.9% respectively, and is among the state-of-the-art techniques using weakly supervised semantic segmentation learning.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision: Waikoloa, HI, January 4-8

First Page

2410

Last Page

2420

ISBN

9798350318920

Identifier

10.1109/WACV57701.2024.00241

Publisher

IEEE

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/WACV57701.2024.00241

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