Explaining and Harnessing Adversarial Examples (FGSM)
Date: 2014-12
Affected: Image classifiers (MNIST, CIFAR), deep neural networks
Attack vector: adversarial-input
Category: research
Severity: High

Goodfellow, Shlens, and Szegedy introduce the Fast Gradient Sign Method, attributing adversarial fragility to model linearity. FGSM produces misclassifying perturbations across many architectures and remains the foundational adversarial-ML attack. ICLR 2015.


Mitigations:

Architectural layers (MAESTRO):