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In this paper we study the problem of invalidating uncertain models with an additive uncertainty. The problem is to check the existence of an uncertainty and a measurement noise which fit to the given model structure and the uncertainty/noise description, as well as the experimental data used for invalidation. We consider a mixed setting in which the uncertainty is characterized in time domain by the l1 induced system norm, while the available data are frequency response samples of the system. We show that this problem, which by formulation poses an infinite-dimensional primal optimization problem, can be solved in a dual, finite-dimensional space with finitely many constraints.