Detecting adversaries in crowdsourcing

作     者：Traganitis, Panagiotis A. Giannakis, Georgios B. 

作者机构：Dept. of Electrical and Computer Engineering Digital Technology Center University of Minnesota MN United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Crowdsourcing 

摘      要：Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries. This work investigates the effects of adversaries on crowdsourced classification, under the popular Dawid and Skene model. The adversaries are allowed to deviate arbitrarily from the considered crowdsourcing model, and may potentially cooperate. To address this scenario, we develop an approach that leverages the structure of second-order moments of annotator responses, to identify large numbers of adversaries, and mitigate their impact on the crowdsourcing task. The potential of the proposed approach is empirically demonstrated on synthetic and real crowdsourcing datasets. Copyright © 2021, The Authors. All rights reserved.