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Register a new userStatistical Detection of Collective Data Fraud
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Added by
Ruoyu Wang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Authors
Ruoyu Wang
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Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world.
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