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ZENODO
Preprint . 2026
Data sources: ZENODO
ZENODO
Preprint . 2026
Data sources: Datacite
ZENODO
Preprint . 2026
Data sources: Datacite
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High-Capacity Steganographic Encoding Using Legal Chess PGN with Authenticated Encryption

Authors: Atharva Sen Barai;

High-Capacity Steganographic Encoding Using Legal Chess PGN with Authenticated Encryption

Abstract

We present a novel steganographic system that embeds encrypted messages within legal chess game notation (PGN) using rule-constrained symbolic encoding. Our method combines AES-256-GCM authenticated encryption with a deterministic chess move selection algorithm that maps large integers to sequences of legal chess moves. The system achieves theoretically unbounded capacity constrained by practical PGN size limits through chess variation trees while maintaining plausible deniability and tamper detection. Experimental results demonstrate successful encoding of messages up to several kilobytes with encoding rates of 4.3 bits per chess ply. The system provides confidentiality through AES-256 encryption, integrity through GCM authentication tags, and deniability through the natural appearance of legal chess games. Unlike traditional media-based steganography, our approach generates syntactically valid chess notation that passes standard PGN validation tools, making detection difficult in practice.

Keywords

cryptography, chess, AES-GCM, steganography, pgn, information hiding

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average