  Transferability captures the ability of an attack against a machine-learning
model to be effective against a different, potentially unknown, model.
Empirical evidence for transferability has been shown in previous work, but the
underlying reasons why an attack transfers or not are not yet well understood.
In this paper, we present a comprehensive analysis aimed to investigate the
transferability of both test-time evasion and training-time poisoning attacks.
We provide a unifying optimization framework for evasion and poisoning attacks,
and a formal definition of transferability of such attacks. We highlight two
main factors contributing to attack transferability: the intrinsic adversarial
vulnerability of the target model, and the complexity of the surrogate model
used to optimize the attack. Based on these insights, we define three metrics
that impact an attack's transferability. Interestingly, our results derived
from theoretical analysis hold for both evasion and poisoning attacks, and are
confirmed experimentally using a wide range of linear and non-linear
classifiers and datasets.
