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Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images

conference contribution
posted on 2023-11-29, 18:08 authored by Tribhuvanesh Orekondy, Mario FritzMario Fritz, Bernt Schiele
Images convey a broad spectrum of personal information. If such images are shared on social media platforms, this personal information is leaked which conflicts with the privacy of depicted persons. Therefore, we aim for automated approaches to redact such private information and thereby protect privacy of the individual. By conducting a user study we find that obfuscating the image regions related to the private information leads to privacy while retaining utility of the images. Moreover, by varying the size of the regions different privacy-utility trade-offs can be achieved. Our findings argue for a "redaction by segmentation" paradigm. Hence, we propose the first sizable dataset of private images "in the wild" annotated with pixel and instance level labels across a broad range of privacy classes. We present the first model for automatic redaction of diverse private information. It is effective at achieving various privacy-utility trade-offs within 83% of the performance of redactions based on ground-truth annotation.

History

Preferred Citation

Tribhuvanesh Orekondy, Mario Fritz and Bernt Schiele. Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2018.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Legacy Posted Date

2018-03-27

Open Access Type

  • Gold

BibTeX

@inproceedings{cispa_all_2590, title = "Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images", author = "Orekondy, Tribhuvanesh and Fritz, Mario and Schiele, Bernt", booktitle="{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}", year="2018", }

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