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A Distributed Biased Boundary Attack Method in Black-Box Attack
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios. Research on black-box attack methods and the generation of adversarial samples is helpful to discover the defects of machine learning models. It can strengthen the robustness of machine learning algorithms models. Such methods require queries frequently, which are less efficient. This paper has madedoi:10.3390/app112110479 fatcat:bniqoj5kbvbwxh6zbxq4gucpmy