ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints. (arXiv:2201.01621v1 [cs.CR])

Advances in deep learning have enabled a wide range of promising
applications. However, these systems are vulnerable to Adversarial Machine
Learning (AML) attacks; adversarially crafted perturbations to their inputs
could cause them to misclassify. Several state-of-the-art adversarial attacks
have demonstrated that they can reliably fool classifiers making these attacks
a significant threat. Adversarial attack generation algorithms focus primarily
on creating successful examples while controlling the noise magnitude and
distribution to make detection more difficult. The underlying assumption of
these attacks …

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