# FoggySight: A Scheme for Facial Lookup Privacy

arXiv: https://arxiv.org/abs/2012.08588 · HTML: https://mlsanity.com/paper/2012.08588/

## Key facts

- Cited by 12 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Data Poisoning Won't Save You From Facial Recognition](https://mlsanity.com/paper/2106.14851/) (2021)
- [SoK: Anti-Facial Recognition Technology](https://mlsanity.com/paper/2112.04558/) (2021)
- [FACE-AUDITOR: Data Auditing in Facial Recognition Systems](https://mlsanity.com/paper/2304.02782/) (2023)
- [Fairness Properties of Face Recognition and Obfuscation Systems](https://mlsanity.com/paper/2108.02707/) (2021)
- [Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems](https://mlsanity.com/paper/2104.08690/) (2021)
- [Assessing Privacy Risks from Feature Vector Reconstruction Attacks](https://mlsanity.com/paper/2202.05760/) (2022)
- [StyleGAN as a Utility-Preserving Face De-identification Method](https://mlsanity.com/paper/2212.02611/) (2022)
- [Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2502.00451/) (2025)
- [Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches](https://mlsanity.com/paper/2202.10320/) (2022)
- [FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods](https://mlsanity.com/paper/2503.08731/) (2025)
- [Personalized Face Privacy Protection From a Single Image](https://mlsanity.com/paper/2605.19032/) (2026)

## Abstract (arXiv)

Advances in deep learning algorithms have enabled better-than-human performance on face recognition tasks. In parallel, private companies have been scraping social media and other public websites that tie photos to identities and have built up large databases of labeled face images. Searches in these databases are now being offered as a service to law enforcement and others and carry a multitude of privacy risks for social media users. In this work, we tackle the problem of providing privacy from such face recognition systems. We propose and evaluate FoggySight, a solution that applies lessons learned from the adversarial examples literature to modify facial photos in a privacy-preserving manner before they are uploaded to social media. FoggySight's core feature is a community protection strategy where users acting as protectors of privacy for others upload decoy photos generated by adversarial machine learning algorithms. We explore different settings for this scheme and find that it does enable protection of facial privacy -- including against a facial recognition service with unknown internals.