{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:02:52Z","timestamp":1771696972042,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T00:00:00Z","timestamp":1609545600000},"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":[[2021,1,2]]},"DOI":"10.1145\/3430984.3431966","type":"proceedings-article","created":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T05:34:44Z","timestamp":1609133684000},"page":"449-453","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Trustworthy AI"],"prefix":"10.1145","author":[{"given":"Richa","family":"Singh","sequence":"first","affiliation":[{"name":"IIT Jodhpur, India"}]},{"given":"Mayank","family":"Vatsa","sequence":"additional","affiliation":[{"name":"IIT Jodhpur, India"}]},{"given":"Nalini","family":"Ratha","sequence":"additional","affiliation":[{"name":"University of Buffalo, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,1,2]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2018. Ethics Guidelines for Trustworthy AI. https:\/\/ec.europa.eu\/futurium\/en\/ai-alliance-consultation  2018. Ethics Guidelines for Trustworthy AI. https:\/\/ec.europa.eu\/futurium\/en\/ai-alliance-consultation"},{"key":"e_1_3_2_1_2_1","unstructured":"2019. What is AI Safety. https:\/\/faculty.ai\/blog\/what-is-ai-safety\/  2019. What is AI Safety. https:\/\/faculty.ai\/blog\/what-is-ai-safety\/"},{"key":"e_1_3_2_1_3_1","unstructured":"2020. Ethics of artificial intelligence. https:\/\/en.wikipedia.org\/wiki\/Ethics_of_artificial_intelligence)  2020. Ethics of artificial intelligence. https:\/\/en.wikipedia.org\/wiki\/Ethics_of_artificial_intelligence)"},{"key":"e_1_3_2_1_4_1","unstructured":"2020. Fairness (machine learning). https:\/\/en.wikipedia.org\/wiki\/Fairness_(machine_learning)  2020. Fairness (machine learning). https:\/\/en.wikipedia.org\/wiki\/Fairness_(machine_learning)"},{"key":"e_1_3_2_1_5_1","unstructured":"2020. Robustness (computer science). https:\/\/en.wikipedia.org\/wiki\/Robustness_(computer_science)  2020. Robustness (computer science). https:\/\/en.wikipedia.org\/wiki\/Robustness_(computer_science)"},{"key":"e_1_3_2_1_6_1","volume-title":"IEEE International Conference on Biometrics: Theory, Applications and Systems.","author":"Agarwal Akshay","unstructured":"Akshay Agarwal , Richa Singh , Mayank Vatsa , and Nalini Ratha . [n.d.]. Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect? . In IEEE International Conference on Biometrics: Theory, Applications and Systems. Akshay Agarwal, Richa Singh, Mayank Vatsa, and Nalini Ratha. [n.d.]. Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?. In IEEE International Conference on Biometrics: Theory, Applications and Systems."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3027183"},{"key":"e_1_3_2_1_8_1","volume-title":"Gradient on the Performance of CNN. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.","author":"Agarwal Akshay","year":"2020","unstructured":"Akshay Agarwal , Mayank Vatsa , and Richa Singh . 2020 . The Role of \u2019Sign\u2019 and \u2019Direction\u2019 of Gradient on the Performance of CNN. In IEEE Conference on Computer Vision and Pattern Recognition Workshops. Akshay Agarwal, Mayank Vatsa, and Richa Singh. 2020. The Role of \u2019Sign\u2019 and \u2019Direction\u2019 of Gradient on the Performance of CNN. In IEEE Conference on Computer Vision and Pattern Recognition Workshops."},{"key":"e_1_3_2_1_9_1","first-page":"1","article-title":"AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models","volume":"21","author":"Arya Vijay","year":"2020","unstructured":"Vijay Arya , Rachel K.\u00a0E. Bellamy , Pin-Yu Chen , Amit Dhurandhar , Michael Hind , Samuel\u00a0 C. Hoffman , Stephanie Houde , Q.\u00a0 Vera Liao , Ronny Luss , Aleksandra Mojsilovi\u00c4\u2021 , Sami Mourad , Pablo Pedemonte , Ramya Raghavendra , John\u00a0 T. Richards , Prasanna Sattigeri , Karthikeyan Shanmugam , Moninder Singh , Kush\u00a0 R. Varshney , Dennis Wei , and Yunfeng Zhang . 2020 . AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models . Journal of Machine Learning Research 21 , 130 (2020), 1 \u2013 6 . http:\/\/jmlr.org\/papers\/v21\/19-1035.html Vijay Arya, Rachel K.\u00a0E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel\u00a0C. Hoffman, Stephanie Houde, Q.\u00a0Vera Liao, Ronny Luss, Aleksandra Mojsilovi\u00c4\u2021, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John\u00a0T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush\u00a0R. Varshney, Dennis Wei, and Yunfeng Zhang. 2020. AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. Journal of Machine Learning Research 21, 130 (2020), 1\u20136. http:\/\/jmlr.org\/papers\/v21\/19-1035.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_10_1","volume-title":"Conference on Fairness, Accountability and Transparency. 77\u201391","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru . 2018 . Gender shades: Intersectional accuracy disparities in commercial gender classification . In Conference on Fairness, Accountability and Transparency. 77\u201391 . Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77\u201391."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3347447.3356752"},{"key":"e_1_3_2_1_12_1","unstructured":"Ana\u00a0Paula Chaves and Marco\u00a0Aur\u00e9lio Gerosa. 2019. How should my chatbot interact? A survey on human-chatbot interaction design. CoRR abs\/1904.02743(2019). arxiv:1904.02743http:\/\/arxiv.org\/abs\/1904.02743  Ana\u00a0Paula Chaves and Marco\u00a0Aur\u00e9lio Gerosa. 2019. How should my chatbot interact? A survey on human-chatbot interaction design. CoRR abs\/1904.02743(2019). arxiv:1904.02743http:\/\/arxiv.org\/abs\/1904.02743"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/91"},{"key":"e_1_3_2_1_14_1","volume-title":"Games for Fairness and Interpretability. In Companion Proceedings of the Web Conference","author":"Chu Eric","year":"2020","unstructured":"Eric Chu , Nabeel Gillani , and Sneha Priscilla\u00a0Makini . 2020 . Games for Fairness and Interpretability. In Companion Proceedings of the Web Conference 2020. 520\u2013524. Eric Chu, Nabeel Gillani, and Sneha Priscilla\u00a0Makini. 2020. Games for Fairness and Interpretability. In Companion Proceedings of the Web Conference 2020. 520\u2013524."},{"key":"e_1_3_2_1_15_1","volume-title":"36th International Conference on Machine Learning. 1310\u20131320","author":"Cohen M.","year":"2019","unstructured":"Jeremy\u00a0 M. Cohen , Elan Rosenfeld , and J.\u00a0 Zico Kolter . 2019 . Certified Adversarial Robustness via Randomized Smoothing . In 36th International Conference on Machine Learning. 1310\u20131320 . Jeremy\u00a0M. Cohen, Elan Rosenfeld, and J.\u00a0Zico Kolter. 2019. Certified Adversarial Robustness via Randomized Smoothing. In 36th International Conference on Machine Learning. 1310\u20131320."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. 1436\u20131445","author":"Creager Elliot","year":"2019","unstructured":"Elliot Creager , David Madras , J\u00f6rn-Henrik Jacobsen , Marissa\u00a0 A Weis , Kevin Swersky , Toniann Pitassi , and Richard Zemel . 2019 . Flexibly fair representation learning by disentanglement . In International Conference on Machine Learning. 1436\u20131445 . Elliot Creager, David Madras, J\u00f6rn-Henrik Jacobsen, Marissa\u00a0A Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel. 2019. Flexibly fair representation learning by disentanglement. In International Conference on Machine Learning. 1436\u20131445."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-0802"},{"key":"e_1_3_2_1_18_1","volume-title":"Demographic bias in biometrics: A survey on an emerging challenge","author":"Drozdowski Pawel","year":"2020","unstructured":"Pawel Drozdowski , Christian Rathgeb , Antitza Dantcheva , Naser Damer , and Christoph Busch . 2020. Demographic bias in biometrics: A survey on an emerging challenge . IEEE Transactions on Technology and Society( 2020 ). Pawel Drozdowski, Christian Rathgeb, Antitza Dantcheva, Naser Damer, and Christoph Busch. 2020. Demographic bias in biometrics: A survey on an emerging challenge. IEEE Transactions on Technology and Society(2020)."},{"key":"e_1_3_2_1_19_1","volume-title":"DeepRing: Protecting Deep Neural Network with Blockchain. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.","author":"Goel Akhil","year":"2019","unstructured":"Akhil Goel , Akshay Agarwal , Mayank Vatsa , Richa Singh , and Nalini Ratha . 2019 . DeepRing: Protecting Deep Neural Network with Blockchain. In IEEE Conference on Computer Vision and Pattern Recognition Workshops. Akhil Goel, Akshay Agarwal, Mayank Vatsa, Richa Singh, and Nalini Ratha. 2019. DeepRing: Protecting Deep Neural Network with Blockchain. In IEEE Conference on Computer Vision and Pattern Recognition Workshops."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS46853.2019.9185999"},{"key":"e_1_3_2_1_21_1","volume-title":"DNDNet: Reconfiguring CNN for Adversarial Robustness. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.","author":"Goel Akhil","year":"2020","unstructured":"Akhil Goel , Akshay Agarwal , Mayank Vatsa , Richa Singh , and Nalini Ratha . 2020 . DNDNet: Reconfiguring CNN for Adversarial Robustness. In IEEE Conference on Computer Vision and Pattern Recognition Workshops. Akhil Goel, Akshay Agarwal, Mayank Vatsa, Richa Singh, and Nalini Ratha. 2020. DNDNet: Reconfiguring CNN for Adversarial Robustness. In IEEE Conference on Computer Vision and Pattern Recognition Workshops."},{"key":"e_1_3_2_1_22_1","volume-title":"International Conference on Learning Representations.","author":"Goodfellow J","year":"2015","unstructured":"Ian\u00a0 J Goodfellow , Jonathon Shlens , and Christian Szegedy . 2015 . Explaining and harnessing adversarial examples (2014) . In International Conference on Learning Representations. Ian\u00a0J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples (2014). In International Conference on Learning Representations."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01160-w"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12341"},{"key":"e_1_3_2_1_25_1","volume-title":"Transparency and reproducibility in artificial intelligence. Nature 586, 7829","author":"Haibe-Kains Benjamin","year":"2020","unstructured":"Benjamin Haibe-Kains , George\u00a0Alexandru Adam , Ahmed Hosny , Farnoosh Khodakarami , Levi Waldron , Bo Wang , Chris McIntosh , Anna Goldenberg , Anshul Kundaje , Casey\u00a0 S Greene , 2020. Transparency and reproducibility in artificial intelligence. Nature 586, 7829 ( 2020 ), E14\u2013E16. Benjamin Haibe-Kains, George\u00a0Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, Levi Waldron, Bo Wang, Chris McIntosh, Anna Goldenberg, Anshul Kundaje, Casey\u00a0S Greene, 2020. Transparency and reproducibility in artificial intelligence. Nature 586, 7829 (2020), E14\u2013E16."},{"key":"e_1_3_2_1_26_1","first-page":"146","article-title":"Chatbots for Customer Service on Hotels\u2019 Websites","volume":"2","author":"Lasek Miroslawa","year":"2013","unstructured":"Miroslawa Lasek and Szymon Jessa . 2013 . Chatbots for Customer Service on Hotels\u2019 Websites . Information Systems in Management 2 , 2 (2013), 146 \u2013 158 . Miroslawa Lasek and Szymon Jessa. 2013. Chatbots for Customer Service on Hotels\u2019 Websites. Information Systems in Management 2, 2 (2013), 146\u2013158.","journal-title":"Information Systems in Management"},{"key":"e_1_3_2_1_27_1","unstructured":"Yi-Shan Lin Wen-Chuan Lee and Z.\u00a0Berkay Celik. 2020. What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors. arxiv:2009.10639\u00a0[cs.CV]  Yi-Shan Lin Wen-Chuan Lee and Z.\u00a0Berkay Celik. 2020. What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors. arxiv:2009.10639\u00a0[cs.CV]"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00496"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_23"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.56"},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Learning Representations.","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry , Aleksandar Makelov , Ludwig Schmidt , Dimitris Tsipras , and Adrian Vladu . 2018 . Towards deep learning models resistant to adversarial attacks . In International Conference on Learning Representations. Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_32_1","unstructured":"Ninareh Mehrabi Fred Morstatter Nripsuta Saxena Kristina Lerman and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635(2019).  Ninareh Mehrabi Fred Morstatter Nripsuta Saxena Kristina Lerman and Aram Galstyan. 2019. A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635(2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks. arXiv:1906.06449","author":"Mejia A.","year":"2019","unstructured":"F.\u00a0 A. Mejia , P. Gamble , Z. Hampel-Arias , M. Lomnitz , N. Lopatina , L. Tindall , and M.\u00a0 A. Barrios . 2019. Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks. arXiv:1906.06449 ( 2019 ). F.\u00a0A. Mejia, P. Gamble, Z. Hampel-Arias, M. Lomnitz, N. Lopatina, L. Tindall, and M.\u00a0A. Barrios. 2019. Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks. arXiv:1906.06449 (2019)."},{"key":"e_1_3_2_1_34_1","volume-title":"Attribute Aware Filter-Drop for Bias Invariant Classification. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 32\u201333","author":"Nagpal Shruti","year":"2020","unstructured":"Shruti Nagpal , Maneet Singh , Richa Singh , and Mayank Vatsa . 2020 . Attribute Aware Filter-Drop for Bias Invariant Classification. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 32\u201333 . Shruti Nagpal, Maneet Singh, Richa Singh, and Mayank Vatsa. 2020. Attribute Aware Filter-Drop for Bias Invariant Classification. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 32\u201333."},{"key":"e_1_3_2_1_35_1","unstructured":"Shruti Nagpal Maneet Singh Richa Singh Mayank Vatsa and Nalini Ratha. 2019. Deep Learning for Face Recognition: Pride or Prejudiced?arXiv preprint arXiv:1904.01219(2019).  Shruti Nagpal Maneet Singh Richa Singh Mayank Vatsa and Nalini Ratha. 2019. Deep Learning for Face Recognition: Pride or Prejudiced?arXiv preprint arXiv:1904.01219(2019)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1356"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2020.00018"},{"key":"e_1_3_2_1_38_1","unstructured":"Aditi Raghunathan Jacob Steinhardt and Percy Liang. 2018. Semidefinite relaxations for certifying robustness to adversarial examples. In Advances in Neural Information Processing Systems. 10877\u201310887.  Aditi Raghunathan Jacob Steinhardt and Percy Liang. 2018. Semidefinite relaxations for certifying robustness to adversarial examples. In Advances in Neural Information Processing Systems. 10877\u201310887."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.12.012"},{"key":"e_1_3_2_1_40_1","unstructured":"Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John Dickerson Christoph Studer Larry\u00a0S. Davis Gavin Taylor and Tom Goldstein. 2019. Adversarial training for free!. In Advances in Neural Information Processing Systems. 3358\u20133369.  Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John Dickerson Christoph Studer Larry\u00a0S. Davis Gavin Taylor and Tom Goldstein. 2019. Adversarial training for free!. In Advances in Neural Information Processing Systems. 3358\u20133369."},{"key":"e_1_3_2_1_41_1","unstructured":"Zheyuan\u00a0Ryan Shi Claire Wang and Fei Fang. 2020. Artificial intelligence for social good: A survey. arXiv preprint arXiv:2001.01818(2020).  Zheyuan\u00a0Ryan Shi Claire Wang and Fei Fang. 2020. Artificial intelligence for social good: A survey. arXiv preprint arXiv:2001.01818(2020)."},{"key":"e_1_3_2_1_42_1","volume-title":"Membership Inference Attacks Against Machine Learning Models. In IEEE Symposium on Security and Privacy. 3\u201318","author":"Shokri Reza","year":"2017","unstructured":"Reza Shokri , Marco Stronati , Congzheng Song , and Vitaly Shmatikov . 2017 . Membership Inference Attacks Against Machine Learning Models. In IEEE Symposium on Security and Privacy. 3\u201318 . Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership Inference Attacks Against Machine Learning Models. In IEEE Symposium on Security and Privacy. 3\u201318."},{"key":"e_1_3_2_1_43_1","volume-title":"On the Robustness of Face Recognition Algorithms Against Attacks and Bias. In AAAI Conference on Artificial Intelligence. 13583\u201313589","author":"Singh Richa","year":"2020","unstructured":"Richa Singh , Akshay Agarwal , Maneet Singh , Shruti Nagpal , and Mayank Vatsa . 2020 . On the Robustness of Face Recognition Algorithms Against Attacks and Bias. In AAAI Conference on Artificial Intelligence. 13583\u201313589 . Richa Singh, Akshay Agarwal, Maneet Singh, Shruti Nagpal, and Mayank Vatsa. 2020. On the Robustness of Face Recognition Algorithms Against Attacks and Bias. In AAAI Conference on Artificial Intelligence. 13583\u201313589."},{"key":"e_1_3_2_1_44_1","volume-title":"Conference on Fairness, Accountability, and Transparency, Mireille Hildebrandt, Carlos Castillo, Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 272\u2013283","author":"Toreini Ehsan","year":"2020","unstructured":"Ehsan Toreini , Mhairi Aitken , Kovila P.\u00a0L. Coopamootoo , Karen Elliott , Carlos\u00a0Gonzalez Zelaya , and Aad van Moorsel . 2020 . The relationship between trust in AI and trustworthy machine learning technologies. In FAT*\u201920 : Conference on Fairness, Accountability, and Transparency, Mireille Hildebrandt, Carlos Castillo, Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 272\u2013283 . Ehsan Toreini, Mhairi Aitken, Kovila P.\u00a0L. Coopamootoo, Karen Elliott, Carlos\u00a0Gonzalez Zelaya, and Aad van Moorsel. 2020. The relationship between trust in AI and trustworthy machine learning technologies. In FAT*\u201920: Conference on Fairness, Accountability, and Transparency, Mireille Hildebrandt, Carlos Castillo, Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 272\u2013283."},{"key":"e_1_3_2_1_45_1","volume-title":"International Conference on Learning Representations.","author":"Tram\u00e8r Florian","year":"2018","unstructured":"Florian Tram\u00e8r , Alexey Kurakin , Nicolas Papernot , Ian\u00a0 J. Goodfellow , Dan Boneh , and Patrick\u00a0 D. McDaniel . 2018 . Ensemble adversarial training: Attacks and defenses . In International Conference on Learning Representations. Florian Tram\u00e8r, Alexey Kurakin, Nicolas Papernot, Ian\u00a0J. Goodfellow, Dan Boneh, and Patrick\u00a0D. McDaniel. 2018. Ensemble adversarial training: Attacks and defenses. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_46_1","unstructured":"Eric Wong Frank\u00a0R. Schmidt Jan\u00a0Hendrik Metzen and J.\u00a0Zico Kolter. 2018. Scaling provable adversarial defenses. In Advances in Neural Information Processing Systems. 8400\u20138409.  Eric Wong Frank\u00a0R. Schmidt Jan\u00a0Hendrik Metzen and J.\u00a0Zico Kolter. 2018. Scaling provable adversarial defenses. In Advances in Neural Information Processing Systems. 8400\u20138409."},{"key":"e_1_3_2_1_47_1","volume-title":"International Conference on Learning Representations. 1\u201316","author":"Xie Cihang","year":"2018","unstructured":"Cihang Xie , Jianyu Wang , Zhishuai Zhang , Zhou Ren , and Alan\u00a0 L. Yuille . 2018 . Mitigating adversarial effects through randomization . In International Conference on Learning Representations. 1\u201316 . Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan\u00a0L. Yuille. 2018. Mitigating adversarial effects through randomization. In International Conference on Learning Representations. 1\u201316."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886017"},{"key":"e_1_3_2_1_50_1","volume-title":"International Conference on Learning Representations.","author":"Zhang Huan","year":"2019","unstructured":"Huan Zhang , Hongge Chen , Zhao Song , Duane\u00a0 S. Boning , Inderjit\u00a0 S. Dhillon , and Cho-Jui Hsieh . 2019 . The limitations of adversarial training and the blind-spot attack . In International Conference on Learning Representations. Huan Zhang, Hongge Chen, Zhao Song, Duane\u00a0S. Boning, Inderjit\u00a0S. Dhillon, and Cho-Jui Hsieh. 2019. The limitations of adversarial training and the blind-spot attack. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_51_1","volume-title":"5555. Database Meets Artificial Intelligence: A Survey","author":"Zhou Xuanhe","year":"2020","unstructured":"Xuanhe Zhou , Chengliang Chai , Guoliang Li , and Ji SUN . 5555. Database Meets Artificial Intelligence: A Survey . IEEE Transactions on Knowledge and Data Engineering ( 5555). https:\/\/doi.org\/10.1109\/TKDE. 2020 .2994641 10.1109\/TKDE.2020.2994641 Xuanhe Zhou, Chengliang Chai, Guoliang Li, and Ji SUN. 5555. Database Meets Artificial Intelligence: A Survey. IEEE Transactions on Knowledge and Data Engineering (5555). https:\/\/doi.org\/10.1109\/TKDE.2020.2994641"}],"event":{"name":"CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD","location":"Bangalore India","acronym":"CODS COMAD 2021"},"container-title":["Proceedings of the 3rd ACM India Joint International Conference on Data Science &amp; Management of Data (8th ACM IKDD CODS &amp; 26th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3430984.3431966","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3430984.3431966","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:44Z","timestamp":1750195484000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3430984.3431966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,2]]},"references-count":51,"alternative-id":["10.1145\/3430984.3431966","10.1145\/3430984"],"URL":"https:\/\/doi.org\/10.1145\/3430984.3431966","relation":{},"subject":[],"published":{"date-parts":[[2021,1,2]]},"assertion":[{"value":"2021-01-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}