Steganography and Probabilistic Risk Analysis: A Game Theoretical Framework for Quantifying Adversary Advantage and Impact

TL;DR

This paper presents a game-theoretic framework for analyzing and quantifying the strategic advantage of adversaries in steganography, enabling better decision-making for secure communication strategies in high-risk environments.

Contribution

It introduces a novel game-theoretic model that integrates adversary advantage quantification and risk analysis, enhancing understanding of strategic interactions in steganography.

Findings

Equilibrium strategies depend on cost-benefit parameters.

Adversary advantage can be quantitatively measured.

Monte Carlo simulations reveal impact of strategic variations.

Abstract

In high-risk environments where unlawful surveillance is prevalent, securing confidential communications is critical. This study introduces a novel steganographic game-theoretic model to analyze the strategic interactions between a defending company and an adversary. By framing the scenario as a non-cooperative game, there is systematic evaluation of optimal strategies for both parties, incorporating costs and benefits such as implementation expenses, potential data leaks, and operational advantages. The derived equilibrium probabilities enable the assessment of success rates, illustrating conditions under which the company benefits from hiding messages or faces increased risks when not implementing steganography. Sensitivity analysis explores how changes in key parameters impact these strategies, enhancing the understanding of decision-making in secure communications. Furthermore, the introduction of an adversary model that quantifies the adversary's advantage using conditional probabilities derived from success rates allows for a quantitative measure of the adversary's effectiveness based on the defender's strategies. By integrating the adversary's advantage into a novel risk analysis framework and employing Monte Carlo simulations, dynamic interactions are captured across advantage scenarios, considering factors like impact factor, steganography effectiveness, and equilibrium probabilities. This comprehensive framework offers practical insights into optimizing security strategies by quantifying potential risk reductions when the adversary is disadvantaged, providing a clear methodology for assessing and mitigating adversarial threats in complex security environments.