  Robustness of machine learning methods is essential for modern practical
applications. Given the arms race between attack and defense methods, one may
be curious regarding the fundamental limits of any defense mechanism. In this
work, we focus on the problem of learning from noise-injected data, where the
existing literature falls short by either assuming a specific attack method or
by over-specifying the learning problem. We shed light on the
information-theoretic limits of adversarial learning without assuming a
particular learning process or attacker. Finally, we apply our general bounds
to a canonical set of non-trivial learning problems and provide examples of
common types of attacks.
