mlsanityData to 7 Oct 2026

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

arXiv 1712.01066 ↗First posted 4 Dec 2017cs.CV cs.CR cs.CYcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 4; 2025: 5; 2026: 6.

24 papers.

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  1. 2021A Study of Face Obfuscation in ImageNetcited by 83
  2. 2022Evaluation Gaps in Machine Learning Practicecited by 48
  3. 2023Ethical Considerations for Responsible Data Curationcited by 17
  4. 2024Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributescited by 7
  5. 2023Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Imagescited by 6
  6. 2024BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairmentscited by 5
  7. 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
  8. 2024Privacy-preserving Optics for Enhancing Protection in Face De-identificationcited by 4
  9. 2022GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservationcited by 2
  10. 2023On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Normscited by 2
  11. 2025Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attackscited by 2
  12. 2021Privacy-preserving Object Detectioncited by 1
12 more
  1. 2024Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensationcited by 1
  2. 2025Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputscited by 1
  3. 2025Through Their Eyes: User Perceptions on Sensitive Attribute Inference of Social Media Videos by Visual Language Modelscited by 1
  4. 2025When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editingcited by 1
  5. 2026Towards Context-Aware Image Anonymization with Multi-Agent Reasoningcited by 1
  6. 2026Vision Language Model Helps Private Information De-Identification in Vision Datacited by 1
  7. 2021Deep Learning Approach Protecting Privacy in Camera-Based Critical Applicationscited by 0
  8. 2025From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Datacited by 0
  9. 2026When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editingcited by 0
  10. 2026CAPED: Context-Aware Privacy Exposure Defense for Mobile GUI Agentscited by 0
  11. 2026PriMobiBench: Characterizing Visual Privacy Leakage in VLM-Driven Mobile GUI Agentscited by 0
  12. 2026Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGBcited by 0