"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice. (arXiv:2212.14315v1 [cs.CR])

Recent years have seen a proliferation of research on adversarial machine
learning. Numerous papers demonstrate powerful algorithmic attacks against a
wide variety of machine learning (ML) models, and numerous other papers propose
defenses that can withstand most attacks. However, abundant real-world evidence
suggests that actual attackers use simple tactics to subvert ML-driven systems,
and as a result security practitioners have not prioritized adversarial ML
defenses.

Motivated by the apparent gap between researchers and practitioners, this
position paper aims to bridge …

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