# Steganography in Game Actions

arXiv: https://arxiv.org/abs/2412.10442 · HTML: https://mlsanity.com/paper/2412.10442/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)

## Built on by (most cited first, 2 of 2)

- [Steganography Beyond Space-Time with Chain of Multimodal AI](https://mlsanity.com/paper/2502.18547/) (2025)
- [Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games](https://mlsanity.com/paper/2605.14283/) (2026)

## Abstract (arXiv)

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 actions seemingly directed toward innocent objectives. Meanwhile, an observer, serving as a decoder, learns to associate behavioural patterns with their respective agents despite their dynamic nature, thereby unveiling the hidden messages. The interactions of agents are governed by the framework of multi-agent reinforcement learning and shaped by feedback from the observer. This framework encapsulates a game-theoretic dilemma, wherein agents face decisions between cooperating to create distinguishable behavioural patterns or defecting to pursue individually optimal yet potentially overlapping episodic actions. As a proof of concept, we exemplify action steganography through the game of labyrinth, a navigation task where subliminal communication is concealed within the act of steering toward a destination, and systematically validate the stego-system in terms of distortion, capacity, secrecy and robustness when subjected to simulated passive and active adversaries.