In this article, we study cooperative deception in swarms, in the context of a two-player zero-sum game played over a directed acyclic graph. A swarm of agents must navigate to goal destinations while misleading an intelligent adversary that observes only partial, aggregated signals and updates its belief over time to optimize disruptive actions. This interaction is formalized as a dynamic game with one-sided partial observability, capturing both coordinated swarm behavior and adaptive adversarial inference over a finite horizon. To compute the equilibrium strategies, we propose an algorithm based on fictitious play, where best responses are computed via linear programming. To address the exponential complexity of multi-stage planning, we introduce a compression technique that maps observation histories into compact information states, ensuring that our algorithm remains effective with such compressed states. This reduction enables efficient equilibrium computation even in long-horizon settings. Simulations demonstrate how swarm-level deception can strategically reduce adversarial effectiveness and support theoretical results on the algorithm’s convergence and complexity.