{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T06:25:51Z","timestamp":1774765551305,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,3]]},"DOI":"10.1145\/3490354.3494417","type":"proceedings-article","created":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T03:50:06Z","timestamp":1651722606000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["AuthSHAP"],"prefix":"10.1145","author":[{"given":"Debasmita","family":"Das","sequence":"first","affiliation":[{"name":"Mastercard"}]},{"given":"Yatin","family":"Kaytal","sequence":"additional","affiliation":[{"name":"Mastercard"}]},{"suffix":"V","given":"Ram","family":"Ganesh","sequence":"additional","affiliation":[{"name":"Mastercard"}]},{"given":"Rohit","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Mastercard"}]},{"given":"Rajesh Kumar","family":"Ranjan","sequence":"additional","affiliation":[{"name":"Mastercard"}]}],"member":"320","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Bracha Shapira, and Lior Rokach.","author":"Antwarg Liat","year":"2020","unstructured":"Liat Antwarg , Ronnie Mindlin Miller , Bracha Shapira, and Lior Rokach. 2020 . Explaining Anomalies Detected by Autoencoders Using SHAP. arXiv:1903.02407 [cs.LG] Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, and Lior Rokach. 2020. Explaining Anomalies Detected by Autoencoders Using SHAP. arXiv:1903.02407 [cs.LG]"},{"key":"e_1_3_2_1_2_1","unstructured":"Ionut Arghire. 2021. Cybercriminals Finding Ways to Bypass 3D Secure Fraud Prevention System. https:\/\/www.securityweek.com\/cybercriminals-finding-ways-bypass-3d-secure-fraud-prevention-system. [Online].  Ionut Arghire. 2021. Cybercriminals Finding Ways to Bypass 3D Secure Fraud Prevention System. https:\/\/www.securityweek.com\/cybercriminals-finding-ways-bypass-3d-secure-fraud-prevention-system. [Online]."},{"key":"e_1_3_2_1_3_1","volume-title":"Fraud Detection: Using advanced analytics to detect and prevent credit card fraud. https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/20\/2017\/07\/Fraud_Detection.pdf. [Online].","year":"2017","unstructured":"Capgemini. 2017 . Fraud Detection: Using advanced analytics to detect and prevent credit card fraud. https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/20\/2017\/07\/Fraud_Detection.pdf. [Online]. Capgemini. 2017. Fraud Detection: Using advanced analytics to detect and prevent credit card fraud. https:\/\/www.capgemini.com\/pt-en\/wp-content\/uploads\/sites\/20\/2017\/07\/Fraud_Detection.pdf. [Online]."},{"key":"e_1_3_2_1_4_1","unstructured":"Francesco Cartella Orlando Anunciacao Yuki Funabiki Daisuke Yamaguchi Toru Akishita and Olivier Elshocht. 2021. Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data. arXiv:2101.08030 [cs.CR]  Francesco Cartella Orlando Anunciacao Yuki Funabiki Daisuke Yamaguchi Toru Akishita and Olivier Elshocht. 2021. Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data. arXiv:2101.08030 [cs.CR]"},{"key":"e_1_3_2_1_5_1","unstructured":"Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay and Debdeep Mukhopadhyay. 2018. Adversarial Attacks and Defences: A Survey. arXiv:1810.00069 [cs.LG]  Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay and Debdeep Mukhopadhyay. 2018. Adversarial Attacks and Defences: A Survey. arXiv:1810.00069 [cs.LG]"},{"key":"e_1_3_2_1_6_1","volume-title":"Houdini: Fooling deep structured prediction models. arXiv preprint arXiv:1707.05373","author":"Cisse Moustapha","year":"2017","unstructured":"Moustapha Cisse , Yossi Adi , Natalia Neverova , and Joseph Keshet . 2017 . Houdini: Fooling deep structured prediction models. arXiv preprint arXiv:1707.05373 (2017). Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. 2017. Houdini: Fooling deep structured prediction models. arXiv preprint arXiv:1707.05373 (2017)."},{"key":"e_1_3_2_1_7_1","unstructured":"Thales DIS. 2008. Is there a trade-off between stopping fraud losses and serving customers? https:\/\/dis-blog.thalesgroup.com\/mobile\/2018\/04\/06\/is-there-a-trade-off-between-stopping-fraud-losses-and-serving-customers\/. [Online].  Thales DIS. 2008. Is there a trade-off between stopping fraud losses and serving customers? https:\/\/dis-blog.thalesgroup.com\/mobile\/2018\/04\/06\/is-there-a-trade-off-between-stopping-fraud-losses-and-serving-customers\/. [Online]."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Yinpeng Dong Xiao Yang Zhijie Deng Tianyu Pang Zihao Xiao Hang Su and Jun Zhu. 2021. Black-box Detection of Backdoor Attacks with Limited Information and Data. arXiv:2103.13127 [cs.CR]  Yinpeng Dong Xiao Yang Zhijie Deng Tianyu Pang Zihao Xiao Hang Su and Jun Zhu. 2021. Black-box Detection of Backdoor Attacks with Limited Information and Data. arXiv:2103.13127 [cs.CR]","DOI":"10.1109\/ICCV48922.2021.01617"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Ivan Fursov Matvey Morozov Nina Kaploukhaya Elizaveta Kovtun Rodrigo Rivera-Castro Gleb Gusev Dmitry Babaev Ivan Kireev Alexey Zaytsev and Evgeny Burnaev. 2021. Adversarial Attacks on Deep Models for Financial Transaction Records. arXiv:2106.08361 [cs.LG]  Ivan Fursov Matvey Morozov Nina Kaploukhaya Elizaveta Kovtun Rodrigo Rivera-Castro Gleb Gusev Dmitry Babaev Ivan Kireev Alexey Zaytsev and Evgeny Burnaev. 2021. Adversarial Attacks on Deep Models for Financial Transaction Records. arXiv:2106.08361 [cs.LG]","DOI":"10.1145\/3447548.3467145"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_1_11_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 ( 2014 ). Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2020.3034721"},{"key":"e_1_3_2_1_13_1","unstructured":"OneSpan Inc. 2020. Customer Experience and Fraud Prevention Top of Priorities for Bankers According to New Survey. https:\/\/www.globenewswire.com\/news-release\/2020\/03\/19\/2003306\/0\/en\/Customer-Experience-and-Fraud-Prevention-Top-of-Priorities-for-Bankers-According-to-New-Survey.html. [Online].  OneSpan Inc. 2020. Customer Experience and Fraud Prevention Top of Priorities for Bankers According to New Survey. https:\/\/www.globenewswire.com\/news-release\/2020\/03\/19\/2003306\/0\/en\/Customer-Experience-and-Fraud-Prevention-Top-of-Priorities-for-Bankers-According-to-New-Survey.html. [Online]."},{"key":"e_1_3_2_1_14_1","unstructured":"Alexey Kurakin Ian Goodfellow and Samy Bengio. 2017. Adversarial Machine Learning at Scale. arXiv:1611.01236 [cs.CV]  Alexey Kurakin Ian Goodfellow and Samy Bengio. 2017. Adversarial Machine Learning at Scale. arXiv:1611.01236 [cs.CV]"},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 31st international conference on neural information processing systems. 4768--4777","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee . 2017 . A unified approach to interpreting model predictions . In Proceedings of the 31st international conference on neural information processing systems. 4768--4777 . Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems. 4768--4777."},{"key":"e_1_3_2_1_16_1","unstructured":"Reinsurance Group of America. 2011. Provider Fraud - Detection and Management. https:\/\/www.rgare.com\/knowledge-center\/media\/articles\/provider-fraud-detection-and-management. [Online].  Reinsurance Group of America. 2011. Provider Fraud - Detection and Management. https:\/\/www.rgare.com\/knowledge-center\/media\/articles\/provider-fraud-detection-and-management. [Online]."},{"key":"e_1_3_2_1_17_1","unstructured":"Piyush Pandita Nimish Awalgaonkar Ilias Bilionis and Jitesh Panchal. 2019. Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design. arXiv:1912.07366 [stat.ML]  Piyush Pandita Nimish Awalgaonkar Ilias Bilionis and Jitesh Panchal. 2019. Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design. arXiv:1912.07366 [stat.ML]"},{"key":"e_1_3_2_1_18_1","volume-title":"UPSET and ANGRI: Breaking high performance image classifiers. arXiv preprint arXiv:1707.01159","author":"Sarkar Sayantan","year":"2017","unstructured":"Sayantan Sarkar , Ankan Bansal , Upal Mahbub , and Rama Chellappa . 2017. UPSET and ANGRI: Breaking high performance image classifiers. arXiv preprint arXiv:1707.01159 ( 2017 ). Sayantan Sarkar, Ankan Bansal, Upal Mahbub, and Rama Chellappa. 2017. UPSET and ANGRI: Breaking high performance image classifiers. arXiv preprint arXiv:1707.01159 (2017)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.22215\/timreview\/1282"},{"key":"e_1_3_2_1_20_1","volume-title":"The shapley value. Handbook of game theory with economic applications 3","author":"Winter Eyal","year":"2002","unstructured":"Eyal Winter . 2002. The shapley value. Handbook of game theory with economic applications 3 ( 2002 ), 2025--2054. Eyal Winter. 2002. The shapley value. Handbook of game theory with economic applications 3 (2002), 2025--2054."}],"event":{"name":"ICAIF'21: 2nd ACM International Conference on AI in Finance","location":"Virtual Event","acronym":"ICAIF'21","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the Second ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490354.3494417","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3490354.3494417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:43Z","timestamp":1750188643000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3490354.3494417"}},"subtitle":["authentication vulnerability detection on tabular data in black box setting"],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":20,"alternative-id":["10.1145\/3490354.3494417","10.1145\/3490354"],"URL":"https:\/\/doi.org\/10.1145\/3490354.3494417","relation":{},"subject":[],"published":{"date-parts":[[2021,11,3]]},"assertion":[{"value":"2022-05-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}