  Adversarial attacks are a major deterrent towards the reliable use of machine
learning models. A powerful type of adversarial attacks is the patch-based
attack, wherein the adversarial perturbations modify localized patches or
specific areas within the images to deceive the trained machine learning model.
In this paper, we introduce Outlier Detection and Dimension Reduction (ODDR), a
holistic defense mechanism designed to effectively mitigate patch-based
adversarial attacks. In our approach, we posit that input features
corresponding to adversarial patches, whether naturalistic or otherwise,
deviate from the inherent distribution of the remaining image sample and can be
identified as outliers or anomalies. ODDR employs a three-stage pipeline:
Fragmentation, Segregation, and Neutralization, providing a model-agnostic
solution applicable to both image classification and object detection tasks.
The Fragmentation stage parses the samples into chunks for the subsequent
Segregation process. Here, outlier detection techniques identify and segregate
the anomalous features associated with adversarial perturbations. The
Neutralization stage utilizes dimension reduction methods on the outliers to
mitigate the impact of adversarial perturbations without sacrificing pertinent
information necessary for the machine learning task. Extensive testing on
benchmark datasets and state-of-the-art adversarial patches demonstrates the
effectiveness of ODDR. Results indicate robust accuracies matching and lying
within a small range of clean accuracies (1%-3% for classification and 3%-5%
for object detection), with only a marginal compromise of 1%-2% in performance
on clean samples, thereby significantly outperforming other defenses.
