Pith Number

pith:Z3VOTPH2

pith:2026:Z3VOTPH27WVQLJATBJW7O52P6U

not attested not anchored not stored refs resolved

AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters

Jacob Lagogiannis, Rosa I. Arriaga, Sauvik Das, William Agnew

AuraMask pipeline produces aesthetic filters that block facial recognition while matching Instagram styles.

arxiv:2605.12937 v1 · 2026-05-13 · cs.CV · cs.AI · cs.HC

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{Z3VOTPH27WVQLJATBJW7O52P6U}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Using AuraMask, we produce 40 ``aesthetic'' filters that emulate popular ``one-click'' Instagram image filters. We show that AuraMask filters meet or exceed the adversarial effectiveness of prior methods against open-source facial recognition models. Moreover, in a controlled online user study (N=630) we confirm these filters achieve significantly higher user acceptance than prior methods.

C2weakest assumption

That effectiveness demonstrated on open-source facial recognition models and acceptance in a controlled online study will generalize to proprietary real-world systems and diverse everyday usage contexts.

C3one line summary

AuraMask produces 40 aesthetic anti-facial recognition filters that match or exceed prior adversarial effectiveness and achieve significantly higher user acceptance in a 630-person study.

References

125 extracted · 125 resolved · 9 Pith anchors

[1] William Agnew, Kevin R McKee, I Gabriel, J Kay, W Isaac, AS Bergman, S El-Sayed, and S Mohamed. 2023. Technologies of Resistance to AI.Equity and Access in Algorithms, Mechanisms, and Optimization(202 2023

[2] Good, Simon King, Mor Naaman, and Rahul Nair 2007

[3] Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey 2018 · arXiv:1801.00553

[4] Recurrent residual convolu- tional neural network based on u-net (r2u-net) for medical image segmentation 2018 · doi:10.48550/arxiv.1802.06955

[5] Bertenthal, and Apu Kapadia 2020

Receipt and verification

First computed	2026-05-18T03:09:09.818545Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ceeae9bcfafdab05a4130a6df7774ff52d4faa3751a258cd028cd71c500a2fd5

Aliases

arxiv: 2605.12937 · arxiv_version: 2605.12937v1 · doi: 10.48550/arxiv.2605.12937 · pith_short_12: Z3VOTPH27WVQ · pith_short_16: Z3VOTPH27WVQLJAT · pith_short_8: Z3VOTPH2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Z3VOTPH27WVQLJATBJW7O52P6U \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ceeae9bcfafdab05a4130a6df7774ff52d4faa3751a258cd028cd71c500a2fd5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b39d316020eaf5f3aa6c9c8ac0375942748c164a375566d888e1ecf5084c25da",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.HC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T03:16:12Z",
    "title_canon_sha256": "b5eb7a1701914019b0f7cc7f253006692ab2d36df3efb6700cc140f177f577cf"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12937",
    "kind": "arxiv",
    "version": 1
  }
}