We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only a relative binary 'win-loss' feedback for this pair, but here this feedback is generated from an arbitrary preference matrix, possibly chosen adversarially. Our main result is an algorithm whose T-round regret compared to the Borda-winner from a set of K items is Õ(K1/3T2/3), as well as a matching Ω(K1/3T2/3) lower bound. We also prove a similar high probability regret bound. We further consider a simpler fixed-gap adversarial setup, which bridges between two extreme preference feedback models for dueling bandits: stationary preferences and an arbitrary sequence of preferences. For the fixed-gap adversarial setup we give an Õ((K/∆2) log T) regret algorithm, where ∆ is the gap in Borda scores between the best item and all other items, and show a lower bound of Ω(K/∆2) indicating that our dependence on the main problem parameters K and ∆ is tight (up to logarithmic factors). Finally, we corroborate the theoretical results with empirical evaluations.