Connecting Pixels to Privacy and Utility:Automatic Redaction of Private Information in Images
Abstract
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 tradeoffs 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 privacyutility trade-offs within 83% of the performance of redactions based on ground-truth annotation