Methods › Computer Vision › Face Privacy › Fawkes

Fawkes

5 papers tagged archive 2025-07-28

Introduced by Shawn Shan et al. in Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Fawkes is an image cloaking system that helps individuals inoculate their images against unauthorized facial recognition models. Fawkes achieves this by helping users add imperceptible pixel-level changes ("cloaks") to their own photos before releasing them. When used to train facial recognition models, these "cloaked" images produce functional models that consistently cause normal images of the user to be misidentified.

PaperSource

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Data Poisoning4
Deep Learning2
Face Recognition2
Privacy Preserving Deep Learning2
Image Generation1
Privacy Preserving1

Usage over time archive 2025-07-28

Papers per year tagged with Fawkes: 2020 to 2023, peak 3 3 0 2020: 1 paper 2020 2021: 3 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Face Privacy

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