Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches. (arXiv:2111.12631v3 [cs.CV] UPDATED)

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. …

arxiv cv detection ensemble unity