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

arXiv: https://arxiv.org/abs/1712.01066 · HTML: https://mlsanity.com/paper/1712.01066/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2017.

## Built on by (most cited first, 24 of 24)

- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes](https://mlsanity.com/paper/2407.03623/) (2024)
- [Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images](https://mlsanity.com/paper/2303.10435/) (2023)
- [BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairments](https://mlsanity.com/paper/2407.18243/) (2024)
- [Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions](https://mlsanity.com/paper/2209.04022/) (2022)
- [Privacy-preserving Optics for Enhancing Protection in Face De-identification](https://mlsanity.com/paper/2404.00777/) (2024)
- [Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attacks](https://mlsanity.com/paper/2512.18264/) (2025)
- [GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservation](https://mlsanity.com/paper/2201.03353/) (2022)
- [On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms](https://mlsanity.com/paper/2310.15848/) (2023)
- [Privacy-preserving Object Detection](https://mlsanity.com/paper/2103.06587/) (2021)
- [Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation](https://mlsanity.com/paper/2412.00277/) (2024)
- [Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputs](https://mlsanity.com/paper/2507.16034/) (2025)
- [Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Models](https://mlsanity.com/paper/2508.07658/) (2025)
- [When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing](https://mlsanity.com/paper/2512.07166/) (2025)
- [Towards Context-Aware Image Anonymization with Multi-Agent Reasoning](https://mlsanity.com/paper/2603.27817/) (2026)
- [Vision Language Model Helps Private Information De-Identification in Vision Data](https://mlsanity.com/paper/2606.09132/) (2026)
- [CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agents](https://mlsanity.com/paper/2606.12666/) (2026)
- [Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications](https://mlsanity.com/paper/2110.01676/) (2021)
- [From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Data](https://mlsanity.com/paper/2510.19418/) (2025)
- [When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing](https://mlsanity.com/paper/2606.07171/) (2026)
- [PriMobiBench: Characterizing Visual Privacy Leakage in VLM-Driven Mobile GUI Agents](https://mlsanity.com/paper/2609.13873/) (2026)
- [Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB](https://mlsanity.com/paper/2609.28360/) (2026)

## Abstract (arXiv)

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.