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Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs. (arXiv:2212.06836v1 [cs.LG])
Dec. 15, 2022, 2:17 a.m. | Hongyan Bao, Yufei Han, Yujun Zhou, Xin Gao, Xiangliang Zhang
cs.CR updates on arXiv.org arxiv.org
Our work targets at searching feasible adversarial perturbation to attack a
classifier with high-dimensional categorical inputs in a domain-agnostic
setting. This is intrinsically an NP-hard knapsack problem where the
exploration space becomes explosively larger as the feature dimension
increases. Without the help of domain knowledge, solving this problem via
heuristic method, such as Branch-and-Bound, suffers from exponential
complexity, yet can bring arbitrarily bad attack results. We address the
challenge via the lens of multi-armed bandit based combinatorial search. Our
proposed …
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