Steganography and Probabilistic Risk Analysis: A Game Theoretical Framework for Quantifying Adversary Advantage and Impact
In environments where adversaries engage in active surveillance and covert communication, defenders face the dual challenge of when to deploy steganography and whether it yields operational benefit. We present a novel game-theoretic model of steganographic operations that captures strategic interactions between a defender and an adversary through calibrated monetary primitives and nonlinear utility mappings. We derive mixed-strategy equilibria that drive conditional and unconditional success rates for hiding and detection, and introduce a time-varying adversarial advantage metric that quantifies when an attacker's incentives temporarily exceed the defender's detection capacity. By linking this advantage to a new currency-unit risk measure, we extend the classical risk formula into a decision-aware, monetised framework. A Monte Carlo simulation pipeline embeds payload shifts, detector learning, and scenario uncertainty to deliver distributions of success probabilities and expected losses rather than a static snapshot. Empirical calibration using breach cost statistics, regulatory fine caps, and expert elicitations supports strategic prescriptions for steganographic deployment, detector investment, and governance trade-offs. Our results provide actionable insight into when steganography strengthens organisational resilience, and when it may yield marginal or negative value.