  In machine learning Trojan attacks, an adversary trains a corrupted model
that obtains good performance on normal data but behaves maliciously on data
samples with certain trigger patterns. Several approaches have been proposed to
detect such attacks, but they make undesirable assumptions about the attack
strategies or require direct access to the trained models, which restricts
their utility in practice.
  This paper addresses these challenges by introducing a Meta Neural Trojan
Detection (MNTD) pipeline that does not make assumptions on the attack
strategies and only needs black-box access to models. The strategy is to train
a meta-classifier that predicts whether a given target model is Trojaned. To
train the meta-model without knowledge of the attack strategy, we introduce a
technique called jumbo learning that samples a set of Trojaned models following
a general distribution. We then dynamically optimize a query set together with
the meta-classifier to distinguish between Trojaned and benign models.
  We evaluate MNTD with experiments on vision, speech, tabular data and natural
language text datasets, and against different Trojan attacks such as data
poisoning attack, model manipulation attack, and latent attack. We show that
MNTD achieves 97% detection AUC score and significantly outperforms existing
detection approaches. In addition, MNTD generalizes well and achieves high
detection performance against unforeseen attacks. We also propose a robust MNTD
pipeline which achieves 90% detection AUC even when the attacker aims to evade
the detection with full knowledge of the system.

Artificial Intelligence;Cryptography and Security;Machine Learning