Attack Agnostic Statistical Method for Adversarial Detection
This addresses the vulnerability of deep learning systems to adversarial attacks, offering a detection solution that is attack-agnostic, though it appears incremental as it builds on existing statistical techniques.
The paper tackles the problem of detecting adversarial attacks in image classification by proposing a statistical method based on per-class feature distributions and various distance metrics, achieving good detection performance on MNIST and CIFAR-10 datasets regardless of attack method, sample size, or perturbation degree.
Deep Learning based AI systems have shown great promise in various domains such as vision, audio, autonomous systems (vehicles, drones), etc. Recent research on neural networks has shown the susceptibility of deep networks to adversarial attacks - a technique of adding small perturbations to the inputs which can fool a deep network into misclassifying them. Developing defenses against such adversarial attacks is an active research area, with some approaches proposing robust models that are immune to such adversaries, while other techniques attempt to detect such adversarial inputs. In this paper, we present a novel statistical approach for adversarial detection in image classification. Our approach is based on constructing a per-class feature distribution and detecting adversaries based on comparison of features of a test image with the feature distribution of its class. For this purpose, we make use of various statistical distances such as ED (Energy Distance), MMD (Maximum Mean Discrepancy) for adversarial detection, and analyze the performance of each metric. We experimentally show that our approach achieves good adversarial detection performance on MNIST and CIFAR-10 datasets irrespective of the attack method, sample size and the degree of adversarial perturbation.