Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization. (arXiv:2206.08575v1 [cs.LG])

We focus on the problem of adversarial attacks against models on discrete
sequential data in the black-box setting where the attacker aims to craft
adversarial examples with limited query access to the victim model. Existing
black-box attacks, mostly based on greedy algorithms, find adversarial examples
using pre-computed key positions to perturb, which severely limits the search
space and might result in suboptimal solutions. To this end, we propose a
query-efficient black-box attack using Bayesian optimization, which dynamically
computes important positions …

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