{"type":"ScholarlyArticle","title":"Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs","authors":[{"name":"Bowen Tian","slug":"bowen-tian","given":"Bowen","family":"Tian","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China"},{"name":"Qinliang Su","slug":"qinliang-su","given":"Qinliang","family":"Su","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Jian Yin","slug":"jian-yin","given":"Jian","family":"Yin","affiliation":"Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China; School of Artificial Intelligence , Sun Yat-sen University , Guangdong , China"}],"abstract":"The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but they are assumed to be collected only from several anomaly types, leaving the majority of anomaly types not represented in the collected anomaly dataset at all. To effectively leverage this kind of incomplete anomalous knowledge represented by the collected anomalies, we propose to learn a probability distribution that can not only model the normal samples, but also guarantee to assign low density values for the collected anomalies. To this end, an anomaly-aware generative adversarial network (GAN) is developed, which, in addition to modeling the normal samples as most GANs do, is able to explicitly avoid assigning probabilities for collected anomalous samples. Moreover, to facilitate the computation of anomaly detection criteria like reconstruction error, the proposed anomaly-aware GAN is designed to be bidirectional, attaching an encoder for the generator.  Extensive experimental results demonstrate that our proposed method is able to effectively make use of the incomplete anomalous information, leading to significant performance gains comparing to existing methods.","doi":"10.24963/ijcai.2022/313","url":"https://ijcai.org/proceedings/2022/313","pdf":"https://ijcai.org/proceedings/2022/0313.pdf","markdown":"https://ijcai.org/proceedings/2022/313.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/313","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Data Mining","track":"Main Track","number":313,"pages":{"first":2255,"last":2261},"keywords":["Data Mining: Anomaly/Outlier Detection","Machine Learning: Generative Adverserial Networks"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986987","length_sec":84,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985130","length_sec":700,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abdallah et al., 2016] Aisha Abdallah, Mohd Aizaini Maarof, and Anazida Zainal. Fraud detection system: A survey. Journal of Network and Computer Applications, 68:90-113, 2016.","title":"Fraud detection system: A survey","authors":"Aisha Abdallah, Mohd Aizaini Maarof, Anazida Zainal","year":"2016","venue":"Journal of Network and Computer Applications","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Akcay et al., 2018] Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon. Ganomaly: Semi-supervised anomaly detection via adversarial training. In ACCV, pages 622-637, 2018.","title":"Ganomaly: Semi-supervised anomaly detection via adversarial training","authors":"Samet Akcay, Toby P Amir Atapour-Abarghouei, Breckon","year":"2018","venue":"ACCV","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[An and Cho, 2015] Jinwon An and Sungzoon Cho. Variational au- toencoder based anomaly detection using reconstruction proba- bility. Special Lecture on IE, 2(1):1-18, 2015.","title":"Jinwon An and Sungzoon Cho. Variational autoencoder based anomaly detection using reconstruction probability","authors":"An, Cho","year":"2015","venue":"Special Lecture on IE","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Das et al., 2020] Shubhomoy Das, Weng-Keen Wong, Thomas Di- etterich, Alan Fern, and Andrew Emmott. Discovering anomalies by incorporating feedback from an expert. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(4):1-32, 2020.","title":"Discovering anomalies by incorporating feedback from an expert","authors":"Das","year":"2020","venue":"ACM Transactions on Knowledge Discovery from Data (TKDD)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Donahue et al., 2017] Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. Adversarial feature learning. In ICLR, 2017.","title":"Adversarial feature learning","authors":"Jeff Donahue, Philipp Krähenbühl, Trevor Darrell","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dumoulin et al., 2017] Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, and Aaron C. Courville. Adversarially learned inference. In ICLR, 2017.","title":"Adversarially learned inference","authors":"Ishmael Vincent Dumoulin, Ben Belghazi, Alex Poole, Martín Lamb, Olivier Arjovsky, Aaron C Mastropietro, Courville","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fernando et al., 2021] Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, and Clinton Fookes. Deep learning for medical anomaly detection-a survey. ACM Comput- ing Surveys (CSUR), 54(7):1-37, 2021.","title":"Deep learning for medical anomaly detection-a survey","authors":"Tharindu Fernando, Harshala Gammulle, Simon Denman, Sridha Sridharan, Clinton Fookes","year":"2021","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Garcia-Teodoro et al., 2009] Pedro Garcia-Teodoro, Jesus Diaz- Verdejo, Gabriel Maciá-Fernández, and Enrique Vázquez. Anomaly-based network intrusion detection: Techniques, sys- tems and challenges. computers & security, 28(1-2):18-28, 2009.","title":"Pedro Garcia-Teodoro, Jesus Diaz-Verdejo, Gabriel Maciá-Fernández, and Enrique Vázquez. Anomaly-based network intrusion detection: Techniques, systems and challenges","authors":"Garcia-Teodoro","year":"2009","venue":"computers & security","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gong et al., 2019] Dong Gong, Lingqiao Liu, Vuong Le, Budha- ditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and An- ton van den Hengel. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In CVPR, pages 1705-1714, 2019.","title":"Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection","authors":"Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton Van Den, Hengel","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurIPS, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Görnitz et al., 2013] Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld. Toward supervised anomaly detection. Jour- nal of Artificial Intelligence Research, 46:235-262, 2013.","title":"Toward supervised anomaly detection","authors":"Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld","year":"2013","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kingma and Welling, 2014] Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In Yoshua Bengio and Yann LeCun, editors, ICLR, 2014.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kingma et al., 2014] Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. Semi-supervised learning with deep generative models. In NeurIPS, pages 3581- 3589, 2014.","title":"Semi-supervised learning with deep generative models","authors":"Shakir Diederik P Kingma, Danilo Mohamed, Max Jimenez Rezende, Welling","year":"2014","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2017] Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, and Lawrence Carin. Alice: Towards understanding adversarial learning for joint distribution matching. NeurIPS, pages 5495-5503, 2017.","title":"Alice: Towards understanding adversarial learning for joint distribution matching","authors":"Chunyuan Li, Hao Liu, Changyou Chen, Yuchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mao et al., 2017] Xudong Mao, Qing Li, Haoran Xie, Ray- mond YK Lau, Zhen Wang, and Stephen Paul Smolley. Least squares generative adversarial networks. In ICCV, pages 2794- 2802, 2017.","title":"Least squares generative adversarial networks","authors":"Xudong Mao, Qing Li, Haoran Xie, Raymond Yk Lau, Zhen Wang, Stephen Paul Smolley","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pang et al., 2021a] Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR), 54(2):1- 38, 2021.","title":"Deep learning for anomaly detection: A review","authors":"Pang","year":"2021","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pang et al., 2021b] Guansong Pang, Anton van den Hengel, Chun- hua Shen, and Longbing Cao. Toward deep supervised anomaly detection: Reinforcement learning from partially la- beled anomaly data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1298-1308, 2021.","title":"Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data","authors":"Pang","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Perera et al., 2019] Pramuditha Perera, Ramesh Nallapati, and Bing Xiang. Ocgan: One-class novelty detection using gans with constrained latent representations. In CVPR, pages 2898-2906, 2019.","title":"Ocgan: One-class novelty detection using gans with constrained latent representations","authors":"Perera","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ruff et al., 2018] Lukas Ruff, Robert Vandermeulen, Nico Goer- nitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classifica- tion. In ICML, pages 4393-4402, 2018.","title":"Deep one-class classification","authors":"Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Ahmed Shoaib, Alexander Siddiqui, Emmanuel Binder, Marius Müller, Kloft","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ruff et al., 2019] Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft. Deep semi-supervised anomaly de- tection. In ICLR, 2019.","title":"Deep semi-supervised anomaly detection","authors":"Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Schlegl et al., 2017] Thomas Schlegl, Philipp Seeböck, Sebas- tian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. Unsupervised anomaly detection with generative adversarial net- works to guide marker discovery. In IPMI, pages 146-157, 2017.","title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","authors":"Thomas Schlegl, Philipp Seeböck, Ursula Sebastian M Waldstein, Georg Schmidt-Erfurth, Langs","year":"2017","venue":"IPMI","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Schölkopf et al., 2001] Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. Esti- mating the support of a high-dimensional distribution. Neural computation, 13(7):1443-1471, 2001.","title":"Estimating the support of a high-dimensional distribution","authors":"Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, Robert C Williamson","year":"2001","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Sinha et al., 2020] Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, and Stefano Ermon. Negative data augmentation. In ICLR, 2020.","title":"Negative data augmentation","authors":"Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Jin Hongxia, Stefano Ermon","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Tax and Duin, 2004] David MJ Tax and Robert PW Duin. Support vector data description. Machine learning, 54(1):45-66, 2004.","title":"Support vector data description","authors":"M J David, Tax, Robert Pw Duin","year":"2004","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xia et al., 2015] Yan Xia, Xudong Cao, Fang Wen, Gang Hua, and Jian Sun. Learning discriminative reconstructions for unsuper- vised outlier removal. In ICCV, pages 1511-1519, 2015.","title":"Learning discriminative reconstructions for unsupervised outlier removal","authors":"Yan Xia, Xudong Cao, Fang Wen, Gang Hua, Jian Sun","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zenati et al., 2018a] Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222, 2018.","title":"Efficient gan-based anomaly detection","authors":"Zenati","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zenati et al., 2018b] Houssam Zenati, Manon Romain, Chuan- Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar. Adver- sarially learned anomaly detection. In ICDM, pages 727-736, 2018.","title":"Adversarially learned anomaly detection","authors":"Zenati","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhai et al., 2016] Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang. Deep structured energy based models for anomaly detection. In ICML, pages 1100-1109, 2016.","title":"Deep structured energy based models for anomaly detection","authors":"Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang","year":"2016","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zong et al., 2018] Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In ICLR, 2018.","title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","authors":"Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}