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Shawn-Shan
fawkes
Python2020–2025
Shawn-Shan / fawkes

fawkes

Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes

Security & PrivacyAI & Machine LearningLearning & Reference
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5Distinct people
5Mentions
1Explicit recommenders
5.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

The original Fawkes ( https://github.com/Shawn-Shan/fawkes ) project came on my radar when I have started working on adversarial examples in deep learning. Fawkes cloaks facial…

m_2000 used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year5 dated mentions

Observed mentions: 2020-07-22 16:00:29 UTC to 2025-10-30 17:26:31 UTC.

0122020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC211
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

5 sources
m_2000·Oct 30, 2025Recommendation phrase
The original Fawkes ( https://github.com/Shawn-Shan/fawkes ) project came on my radar when I have started working on adversarial examples in deep learning. Fawkes cloaks facial images by adding pixel-based perturbations to the original images, barely visible to the human eye. This bypasses face-recognition systems (or at least weakens their confidence). I highly recommend reading the original paper available from the developer's website: https://sandlab.cs.uchicago.edu/fawkes/ to understand how it works. Generally speaking, Fawkes computes _cloaks_ by maximizing feature similarity to unrelated faces while minimizing DSSIM (Structural Dissimilarity). These cloaks are then applied to the original images, producing cloaked images. These cloaked images look as similar as possible to the original images, whereas their feature space deviate "as
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maximal as possible" from the original. DISCLAIMER: I am not the developer of Fawkes. I merely developed a web-interface for it. Big thanks to the [original researchers from SANDLAB, Chicago: https://people.cs.uchicago.edu/%7Eravenben/publications/abst... Yes, the trained model is part of the repo. Just in case you're irritated by it's size (~300MB).
In Show HN: Bypassing face recognition using Fawkes – Now with web interface
mel_llaguno·Apr 29, 2021Submission

Ask HN: Amyone successfully verify fawkes claims of protection against AI?

I was working on a web frontend for fawkes (https://pypi.org/project/fawkes/) based on the research outlined here (https://github.com/Shawn-Shan/fawkes) which claims to be able to use image level perturbations to protect against AI image recognition systems. From the paper, the following claim is made: "Our cloaking technique is not designed to fool a tracker who uses similarity matching. However, we believe our cloaking technique should still be effective against Amazon Rekognition, since cloaks create a feature space separation between original and cloaked images that should result in low similarity scores between them." I was in the process of verifying this when I found that _no such guarantee_ can be made using fawkes. I documented my experiment here: https://github.com/Shawn-Shan/fawkes/issues/125. Using Azure Face and AWS Rekogniti
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on, perturbed images regardless of the settings resulted in high degrees of similarity. In addition, I did a separate test using a perturbed image and another image with the same subject which _still resulted in a high degree of similarity_. While the README claims "Python scripts that can test the protection effectiveness will be ready shortly.", no such scripts have been provided. According to the fawkes website, their application has been downloaded over 300k times. If the protection they claim to provide is ineffective, then there are _a lot of people_ exposed out there with a misguided sense of being protected. This situation is further complicated by the apparent legitimacy of this tool due to its presentation at Usenix 2020 and its inclusion in pypi. My question is this - has anyone actually validated the efficacy of this algorithm and have reproducible results to share? Or is fawkes yet another example of AI snake oil?
1 points · Open discussion

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