Detecting adversaries in Crowdsourcing
Detecting adversaries in Crowdsourcing
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DOI：
10.1109/icdm51629.2021.00174
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Panagiotis A. Traganitis;G. Giannakis
中科院分区：
文献类型：
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作者：
Panagiotis A. Traganitis;G. Giannakis
尽管众包在各种机器学习和数据科学任务中取得了成功，但它可能容易受到来自专门对手的攻击。这项工作调查的影响，众包分类的对手，在流行的Dawid和Skene模型。允许对手任意偏离所考虑的众包模型，并且可能潜在地合作。为了解决这种情况下，我们开发了一种方法，利用注释者响应的二阶矩的结构，以识别大量的对手，并减轻他们对众包任务的影响。在合成和真实的众包数据集上实证了所提出方法的潜力。
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.