  In Stackelberg security games when information about the attacker's payoffs
is uncertain, algorithms have been proposed to learn the optimal defender
commitment by interacting with the attacker and observing their best responses.
In this paper, we show that, however, these algorithms can be easily
manipulated if the attacker responds untruthfully. As a key finding, attacker
manipulation normally leads to the defender learning a maximin strategy, which
effectively renders the learning attempt meaningless as to compute a maximin
strategy requires no additional information about the other player at all. We
then apply a game-theoretic framework at a higher level to counteract such
manipulation, in which the defender commits to a policy that specifies her
strategy commitment according to the learned information. We provide a
polynomial-time algorithm to compute the optimal such policy, and in addition,
a heuristic approach that applies even when the attacker's payoff space is
infinite or completely unknown. Empirical evaluation shows that our approaches
can improve the defender's utility significantly as compared to the situation
when attacker manipulation is ignored.
