  We design blackbox transfer-based targeted adversarial attacks for an
environment where the attacker's source model and the target blackbox model may
have disjoint label spaces and training datasets. This scenario significantly
differs from the "standard" blackbox setting, and warrants a unique approach to
the attacking process. Our methodology begins with the construction of a class
correspondence matrix between the whitebox and blackbox label sets. During the
online phase of the attack, we then leverage representations of highly related
proxy classes from the whitebox distribution to fool the blackbox model into
predicting the desired target class. Our attacks are evaluated in three complex
and challenging test environments where the source and target models have
varying degrees of conceptual overlap amongst their unique categories.
Ultimately, we find that it is indeed possible to construct targeted
transfer-based adversarial attacks between models that have non-overlapping
label spaces! We also analyze the sensitivity of attack success to properties
of the clean data. Finally, we show that our transfer attacks serve as powerful
adversarial priors when integrated with query-based methods, markedly boosting
query efficiency and adversarial success.
