Synthetic Dataset Generation for Adversarial Machine Learning Research. (arXiv:2207.10719v1 [cs.CV])

Existing adversarial example research focuses on digitally inserted
perturbations on top of existing natural image datasets. This construction of
adversarial examples is not realistic because it may be difficult, or even
impossible, for an attacker to deploy such an attack in the real-world due to
sensing and environmental effects. To better understand adversarial examples
against cyber-physical systems, we propose approximating the real-world through
simulation. In this paper we describe our synthetic dataset generation tool
that enables scalable collection of such …

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