Steganography in Game Actions
Ching-Chun Chang, Isao Echizen

TL;DR
This paper introduces a novel steganographic method using multi-agent reinforcement learning where agents encode hidden messages through their actions in a game environment, and an observer decodes these messages despite dynamic behaviors.
Contribution
It extends steganography to multi-agent environments by embedding messages in agent actions and demonstrates its effectiveness in a labyrinth navigation game.
Findings
Effective concealment of messages within agent actions
High capacity and robustness against passive and active adversaries
Successful decoding of hidden messages by observers
Abstract
The exchange of messages has always carried with it the timeless challenge of secrecy. From whispers in shadows to the enigmatic notes written in the margins of history, humanity has long sought ways to convey thoughts that remain imperceptible to all but the chosen few. The challenge of subliminal communication has been addressed in various forms of steganography. However, the field faces a fundamental paradox: as the art of concealment advances, so too does the science of revelation, leading to an ongoing evolutionary interplay. This study seeks to extend the boundaries of what is considered a viable steganographic medium. We explore a steganographic paradigm, in which hidden information is communicated through the episodes of multiple agents interacting with an environment. Each agent, acting as an encoder, learns a policy to disguise the very existence of hidden messages within…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Chaos-based Image/Signal Encryption · Video Analysis and Summarization
