  A key challenge in computer vision and deep learning is the definition of
robust strategies for the detection of adversarial examples. Here, we propose
the adoption of ensemble approaches to leverage the effectiveness of multiple
detectors in exploiting distinct properties of the input data. To this end, the
ENsemble Adversarial Detector (ENAD) framework integrates scoring functions
from state-of-the-art detectors based on Mahalanobis distance, Local Intrinsic
Dimensionality, and One-Class Support Vector Machines, which process the hidden
features of deep neural networks. ENAD is designed to ensure high
standardization and reproducibility to the computational workflow. Importantly,
extensive tests on benchmark datasets, models and adversarial attacks show that
ENAD outperforms all competing methods in the large majority of settings. The
improvement over the state-of-the-art and the intrinsic generality of the
framework, which allows one to easily extend ENAD to include any set of
detectors, set the foundations for the new area of ensemble adversarial
detection.
