From 0e8229e13e1e7c6443785890ad9bf5db104c5c24 Mon Sep 17 00:00:00 2001
From: Laan Tungir <laantungir@proton.md>
Date: Sun, 28 Jun 2026 20:30:26 -0400
Subject: [PATCH] Update inspiration FAQ to reference waxwing's Primal post and
 the Meteor paper

---
 www/js/version.json        |  6 ++---
 www/llm-steganography.html | 49 ++++++++++++++++++++------------------
 2 files changed, 29 insertions(+), 26 deletions(-)

diff --git a/www/js/version.json b/www/js/version.json
index 14df189..8599883 100644
--- a/www/js/version.json
+++ b/www/js/version.json
@@ -1,5 +1,5 @@
 {
-  "VERSION": "v0.7.61",
-  "VERSION_NUMBER": "0.7.61",
-  "BUILD_DATE": "2026-06-29T00:27:13.001Z"
+  "VERSION": "v0.7.62",
+  "VERSION_NUMBER": "0.7.62",
+  "BUILD_DATE": "2026-06-29T00:30:26.801Z"
 }
diff --git a/www/llm-steganography.html b/www/llm-steganography.html
index 250fef8..3c24872 100644
--- a/www/llm-steganography.html
+++ b/www/llm-steganography.html
@@ -744,35 +744,38 @@
             </button>
             <div class="stegoCollapsibleContent stegoHidden">
               <p>
-                This project is inspired by research into <strong>linguistic
-                steganography</strong> — the art of hiding information within natural
-                language. Traditional steganography hides data in images, audio, or
-                file metadata, but text-based steganography is harder because text is
-                compact and every character is visible.
+                The direct inspiration for this project is a
+                <a href="https://primal.net/e/nevent1qqstxmgsd0egtq57gj8mghckthsv79dhegh7xh97hwtwve6l56g4s9sqtjd7k" target="_blank" rel="noopener">post by waxwing on Primal</a>
+                that walks through the core idea of using a language model's
+                next-token probability distribution as a steganographic carrier
+                channel. The post explains how secret bits can steer token selection
+                while preserving the model's output distribution, making the
+                resulting cover text statistically indistinguishable from normal
+                model output.
               </p>
               <p>
-                The breakthrough idea is to use a <strong>language model's own
-                probability distribution</strong> as the carrier channel. Instead of
-                tweaking pixels or LSBs, you steer which token the model emits next,
-                encoding one secret bit per token. Key inspirations include:
+                That post references the academic paper that formalized this
+                approach:
               </p>
               <ul>
-                <li><strong>Entropy coding approaches</strong> (e.g. Cachin's
-                    information-theoretic steganography) that treat the cover as a
-                    probability distribution and map secret bits onto it.</li>
-                <li><strong>"Dissociated Press" / Markov-chain steganography</strong>
-                    — early experiments that used statistical text generation to hide
-                    messages, limited by the poor quality of pre-LLM language models.</li>
-                <li><strong>Modern LLM steganography research</strong> — papers that
-                    use GPT-style models to achieve near-perfect cover text quality
-                    while encoding multiple bits per token via arithmetic or
-                    half-splitting codes.</li>
+                <li>
+                  <strong><a href="https://eprint.iacr.org/2021/686" target="_blank" rel="noopener">Meteor: A Simple and Practical Steganographic Protocol for LLMs</a></strong>
+                  — this paper introduces the entropy-coding mechanism that makes
+                  the output distribution-preserving. Instead of a naive 2-way
+                  split, the full scheme uses arithmetic coding over the model's
+                  probability distribution, achieving roughly 2–4 bits per token
+                  while remaining information-theoretically undetectable to a censor
+                  who only observes the channel.
+                </li>
               </ul>
               <p>
-                The half-splitting scheme used here is one of the simplest robust
-                approaches: it guarantees exactly one bit per token, is symmetric
-                (encoder and decoder run the same logic), and produces text that's
-                indistinguishable from normal model output to a casual reader.
+                The half-splitting scheme implemented in this demo is a simplified
+                version of Meteor's approach: it guarantees exactly one bit per
+                token, is symmetric (encoder and decoder run the same logic), and
+                produces text that's indistinguishable from normal model output to
+                a casual reader. The full arithmetic-coding version from the paper
+                is more efficient but the core principle is identical — secret bits
+                steer selection within the model's probability distribution.
               </p>
             </div>
           </div>