PrivAttNet: Predicting Privacy Risks in Images Using Visual Attention

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PrivAttNet: Predicting Privacy Risks in Images Using Visual Attention
Title:
PrivAttNet: Predicting Privacy Risks in Images Using Visual Attention
Journal Title:
2020 25th International Conference on Pattern Recognition (ICPR)
Keywords:
Publication Date:
05 May 2021
Citation:
Chen, Z., Kandappu, T., & Subbaraju, V. (2021). PrivAttNet: Predicting Privacy Risks in Images Using Visual Attention. 2020 25th International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/icpr48806.2021.9412925
Abstract:
Visual privacy concerns associated with image sharing is a critical issue that need to be addressed to enable safe and lawful use of online social platforms. Users of social media platforms often suffer from no guidance in sharing sensitive images in public, and often face with social and legal consequences. Given the recent success of visual attention based deep learning methods in measuring abstract phenomena like image memorability, we are motivated to investigate whether visual attention based methods could be useful in measuring psychophysical phenomena like “privacy sensitivity”. In this paper we propose PrivAttNet – a visual attention based approach, that can be trained end-to-end to estimate the privacy sensitivity of images without explicitly detecting sensitive objects and attributes present in the image. We show that our PrivAttNet model outperforms various SOTA and baseline strategies – a 1.6 fold reduction in L1 − error over SOTA and 7%–10% improvement in Spearman-rank correlation between the predicted and ground truth sensitivity scores. Additionally, the attention maps from PrivAttNet are found to be useful in directing the users to the regions that are responsible for generating the privacy risk score.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Career Development Award
Grant Reference no. : 202D800021

This research / project is supported by the Ministry of Education - Academic Research Fund (AcRF) Tier 1
Grant Reference no. : N.A
Description:
© 2022 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:
1051-4651
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