| Title | STATISTICAL DETECTION OF COLLECTIVE DATA FRAUD |
| Authors | Wang, Ruoyu Hu, Xiaobo Sun, Daniel Li, Guoqiang Wong, Raymond Chen, Shiping Liu, Jianquan |
| Affiliation | Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China Univ New South Wales, Sydney, NSW, Australia Peking Univ, Beijing, Peoples R China Enhitech Co Ltd, Shanghai 200241, Peoples R China CSIRO, Data61, Canberra, ACT, Australia NEC Corp Ltd, Biometr Res Labs, Minato City, Tokyo, Japan |
| Issue Date | 2020 |
| Publisher | 2020 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) |
| Abstract | 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. |
| URI | http://hdl.handle.net/20.500.11897/604525 |
| ISBN | 978-1-7281-1331-9 |
| ISSN | 1945-7871 |
| Indexed | CPCI-S(ISTP) |
| Appears in Collections: | 待认领 |
