# Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images Source: [Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:51:10.308Z Authors: Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele Published: 2017-12-04T13:35:39.000Z ## 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 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Privacy Protection on VPI-COCO (test)](): Privacy Protection; VPI-COCO (test); PAR: 69.72. ## Original sources and implementation [Original paper]() [PDF]()