This letter investigates methods to detect graph topological changes without making any assumption on the nature of the change itself. To accomplish this, we merge recently developed tools in graph signal processing with matched subspace detection theory and propose two blind topology change detectors. The first detector exploits the prior information that the observed signal is sparse w.r.t. the graph Fourier transform of the nominal graph, while the second makes use of the smoothness prior w.r.t. the nominal graph to detect topological changes. Both detectors are compared with their respective nonblind counterparts in a synthetic scenario that mimics brain networks. The absence of information about the alternative graph, in some cases, might heavily influence the blind detector's performance. However, in cases where the observed signal deviates slightly from the nonblind model, the information about the alternative graph turns out to be not useful.