  We study the problem of mismatched binary hypothesis testing between i.i.d.
distributions. We analyze the tradeoff between the pairwise error probability
exponents when the actual distributions generating the observation are
different from the distributions used in the likelihood ratio test, sequential
probability ratio test, and Hoeffding's generalized likelihood ratio test in
the composite setting. When the real distributions are within a small
divergence ball of the test distributions, we find the deviation of the
worst-case error exponent of each test with respect to the matched error
exponent. In addition, we consider the case where an adversary tampers with the
observation, again within a divergence ball of the observation type. We show
that the tests are more sensitive to distribution mismatch than to adversarial
observation tampering.
