laantungir/client
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update inspiration FAQ to reference waxwing's Primal post and the Meteor paper

Browse Source
This commit is contained in:
Laan Tungir
2026-06-28 20:30:26 -04:00
parent 230bd70e07
commit 0e8229e13e
2 changed files with 29 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
www/js/version.json
View File

@@ -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"
}

49
www/llm-steganography.html
View File

@@ -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>