Summary of Steganography in Game Actions, by Ching-chun Chang and Isao Echizen
Steganography in Game Actions
by Ching-Chun Chang, Isao Echizen
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study extends the scope of viable steganographic mediums by exploring a paradigm where hidden information is communicated through the interactions of multiple agents in an environment. These agents learn policies to disguise hidden messages within seemingly innocuous actions, while an observer learns to associate behavioral patterns with the agents and uncover the hidden messages. The framework uses multi-agent reinforcement learning and feedback from the observer, creating a game-theoretic dilemma where agents must decide between cooperation or individual optimization. To demonstrate action steganography, the study uses the labyrinth navigation task, validating its performance through experimental evaluations of distortion, capacity, secrecy, and robustness against passive and active adversaries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Steganography is like hiding messages in plain sight! This study takes it to the next level by using many “actors” (think game characters) that work together or alone to hide information within their actions. It’s a bit like a secret code hidden within what looks like ordinary behavior. The researchers used a game called Labyrinth, where players try to navigate through a maze, to show how this works. They tested it to see if they could make the hidden messages hard to find and showed that it was possible. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
