---
id: "hardman-measuring-the-glance-an-adversarial-estimate-of"
type: "paper"
type_label: "Papers and paper collections"
title: "Measuring the Glance: An Adversarial Estimate of Habituated Perceptual Entropy in entviz and SSH Randomart"
maintainer: "Daniel Hardman"
authors:
  - "Daniel Hardman"
date: "2026-06-22"
year: 2026
reading_level: "implementer"
reading_level_label: "Implementer"
reading_level_meaning: "developers working in or against the code"
evidence: "informative"
evidence_meaning: "an author's paper, deck, tutorial or post explaining the suite"
status: "active"
status_meaning: "changed within the last six months, or a live service"
access: "public"
access_meaning: "anyone with the link"
topics:
  - "cryptography"
  - "theory"
roles:
  - "developer"
  - "security-architect"
  - "researcher"
source_url: "https://dhh1128.github.io/papers/m-glance.html"
source_license: null
source_license_note: null
found_on: "hardman-papers"
kerisse_credit: false
abstract_license: "CC BY 4.0"
abstract_license_url: "https://creativecommons.org/licenses/by/4.0/"
abstract_attribution: "KSKB, the KERISuite Knowledge Base (https://kskb.dev/)"
last_checked: "2026-10-15"
kskb_url: "https://kskb.dev/cards/hardman-measuring-the-glance-an-adversarial-estimate-of/"
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---

# Measuring the Glance: An Adversarial Estimate of Habituated Perceptual Entropy in entviz and SSH Randomart

A 2026 paper by Daniel Hardman estimating, from an attacker's side, how many bits a habituated glance at an entviz figure or an SSH randomart image actually checks, using a certified reimplementation of the renderer and a grinder that counts the hashing work to forge each channel. It reports a curve rather than a single figure and states that all perceptual tolerances are modeled, not measured on people. It is for designers of human verification of keys and identifiers.

Labels: reading level Implementer; evidence informative (an author's paper, deck, tutorial or post explaining the suite).
Source: https://dhh1128.github.io/papers/m-glance.html (license not recorded).
The abstract is KSKB's own text under Creative Commons Attribution 4.0 (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/; credit KSKB, the KERISuite Knowledge Base (https://kskb.dev/).