PrivObfNet: A Weakly Supervised Semantic Segmentation Model for Data Protection

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PrivObfNet: A Weakly Supervised Semantic Segmentation Model for Data Protection
Title:
PrivObfNet: A Weakly Supervised Semantic Segmentation Model for Data Protection
Journal Title:
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Keywords:
Publication Date:
09 April 2024
Citation:
Tay, C., Subbaraju, V., & Kandappu, T. (2024, January 3). PrivObfNet: A Weakly Supervised Semantic Segmentation Model for Data Protection. 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv57701.2024.00241
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Career development fund
Grant Reference no. : 202D800021
Description:
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2642-9381
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