As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previously-proposed end-to-end detection frameworks have shown to be effective in spotting attacks for one particular spoofing strategy, they have relied on different models, architectures, and speech representations, depending on the spoofing strategy. In practice, however, one does not have \textit{a priori} information regarding the strategy an attacker might employ to fool a speaker recognizer, thus it is necessary to devise approaches which are able to detect attacks regardless of the strategy employed to generate them. In this work, we introduce an end-to-end ensemble based approach such that two models -- previously shown to perform well on each considered attack strategy -- are trained jointly, while a third model learns how to mix their outputs yielding a single score. Experimental results with replay and text-to-speech/voice conversion attacks show the proposed ensemble method achieving similar or superior performance when compared to systems specialized on each spoofing strategy separately.