Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
This addresses privacy concerns for individuals sharing images on social media by proposing an automated redaction method, though it appears incremental as it builds on segmentation techniques.
The paper tackles the problem of automatically redacting private information in images to protect privacy while retaining utility, finding through a user study that obfuscating specific image regions achieves this trade-off, with varying region sizes enabling different privacy-utility balances.
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.