Consider a setup in which a central estimator seeks to estimate an unknown deterministic parameter using measurements from multiple sensors. Some of the sensors may be adversarial in that their utility increases with the Euclidean distance between the estimate of the central estimator and their own local estimate. These sensors may misreport their measurements to the central estimator at a falsification cost. We formulate a Stackelberg game in which the central estimator acts as the leader and the adversarial sensors act as the follower. We present the optimal linear fusion scheme for the estimator and the optimal attack pattern for the adversarial sensors in the Nash equilibrium sense. Interestingly, the estimate at the central estimator may be better than if the measurements from the adversarial sensors were altogether ignored.