Toward Image Privacy Classification and Spatial Attribution of Private Content

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine labeling of image content as private or public is a notoriously difficult problem, with the usual image processing challenges compounded by the highly personal, subjective, and contextual nature of access control decision making. In general, a user's privacy expectation for a given image is consequential to specific contents therein and the presence of sensitive content somewhere in the image is sufficient to warrant a private label. In this work, we extend the problem of determining a single privacy label for a given image to jointly inferring a privacy label and detecting the specific areas of sensitive content within a privately labeled image. We propose a stochastic spatial attribution model which exploits sophisticated (deep neural net derived) image features over randomly selected image patches, as well as image saliency quantification. We validate our detected private regions through extensive user study experiments. This effort to achieve spatial attribution of private image content helps to lay a foundation for warning mechanisms which may serve to aid both social media sites and their users.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1351-1360
Number of pages10
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period12/9/1912/12/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Zhong, H., Li, H., Squicciarini, A., Rajtmajer, S., & Miller, D. (2019). Toward Image Privacy Classification and Spatial Attribution of Private Content. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 1351-1360). [9006510] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006510