{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T21:20:00Z","timestamp":1773696000301,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"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":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467214","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:08Z","timestamp":1628748728000},"page":"1852-1862","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Indirect Invisible Poisoning Attacks on Domain Adaptation"],"prefix":"10.1145","author":[{"given":"Jun","family":"Wu","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingrui","family":"He","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"A Theory of Learning from Different Domains. Machine learning 79, 1--2","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David , John Blitzer , Koby Crammer , Alex Kulesza , Fernando Pereira , and Jennifer Wortman Vaughan . 2010. A Theory of Learning from Different Domains. Machine learning 79, 1--2 ( 2010 ), 151--175. Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A Theory of Learning from Different Domains. Machine learning 79, 1--2 (2010), 151--175."},{"key":"e_1_3_2_2_2_1","unstructured":"Battista Biggio B Nelson and P Laskov. 2012. Poisoning Attacks against Support Vector Machines. In ICML.  Battista Biggio B Nelson and P Laskov. 2012. Poisoning Attacks against Support Vector Machines. In ICML."},{"key":"e_1_3_2_2_3_1","volume-title":"Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy. 39--57","author":"Carlini Nicholas","year":"2017","unstructured":"Nicholas Carlini and David Wagner . 2017 . Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy. 39--57 . Nicholas Carlini and David Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy. 39--57."},{"key":"e_1_3_2_2_4_1","unstructured":"Xinyang Chen Sinan Wang Mingsheng Long and Jianmin Wang. 2019. Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. In ICML.  Xinyang Chen Sinan Wang Mingsheng Long and Jianmin Wang. 2019. Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. In ICML."},{"key":"e_1_3_2_2_5_1","unstructured":"Alireza Fallah Aryan Mokhtari and Asuman Ozdaglar. 2020. On the Convergence Theory of Gradient-based Model-agnostic Meta-learning Algorithms. In AISTATS.  Alireza Fallah Aryan Mokhtari and Asuman Ozdaglar. 2020. On the Convergence Theory of Gradient-based Model-agnostic Meta-learning Algorithms. In AISTATS."},{"key":"e_1_3_2_2_6_1","unstructured":"Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic Meta-learning for Fast Adaptation of Deep Networks. In ICML.  Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic Meta-learning for Fast Adaptation of Deep Networks. In ICML."},{"key":"e_1_3_2_2_7_1","unstructured":"Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In ICML. 1180--1189.  Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In ICML. 1180--1189."},{"key":"e_1_3_2_2_8_1","unstructured":"Rong Ge Furong Huang Chi Jin and Yang Yuan. 2015. Escaping from Saddle Points-online Stochastic Gradient for Tensor Decomposition. In COLT. 797--842.  Rong Ge Furong Huang Chi Jin and Yang Yuan. 2015. Escaping from Saddle Points-online Stochastic Gradient for Tensor Decomposition. In COLT. 797--842."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2599532"},{"key":"e_1_3_2_2_10_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NeurIPS.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In NeurIPS."},{"key":"e_1_3_2_2_11_1","unstructured":"Ian J Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In ICLR.  Ian J Goodfellow Jonathon Shlens and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. In ICLR."},{"key":"e_1_3_2_2_12_1","unstructured":"Arthur Gretton Dino Sejdinovic Heiko Strathmann Sivaraman Balakrishnan Massimiliano Pontil Kenji Fukumizu and Bharath K Sriperumbudur. 2012. Optimal Kernel Choice for Large-scale Two-sample Tests. In NeurIPS. 1205--1213.  Arthur Gretton Dino Sejdinovic Heiko Strathmann Sivaraman Balakrishnan Massimiliano Pontil Kenji Fukumizu and Bharath K Sriperumbudur. 2012. Optimal Kernel Choice for Large-scale Two-sample Tests. In NeurIPS. 1205--1213."},{"key":"e_1_3_2_2_13_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. 770--778.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR. 770--778."},{"key":"e_1_3_2_2_14_1","unstructured":"Fredrik D Johansson David Sontag and Rajesh Ranganath. 2019. Support and Invertibility in Domain-Invariant Representations. In AISTATS.  Fredrik D Johansson David Sontag and Rajesh Ranganath. 2019. Support and Invertibility in Domain-Invariant Representations. In AISTATS."},{"key":"e_1_3_2_2_15_1","unstructured":"Xuanqing Liu Si Si Jerry Zhu Yang Li and Cho-Jui Hsieh. 2019. A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. In NeurIPS. 9780--9790.  Xuanqing Liu Si Si Jerry Zhu Yang Li and Cho-Jui Hsieh. 2019. A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. In NeurIPS. 9780--9790."},{"key":"e_1_3_2_2_16_1","unstructured":"Mingsheng Long Yue Cao Jianmin Wang and Michael I Jordan. 2015. Learning Transferable Features with Deep Adaptation Networks. In ICML.  Mingsheng Long Yue Cao Jianmin Wang and Michael I Jordan. 2015. Learning Transferable Features with Deep Adaptation Networks. In ICML."},{"key":"e_1_3_2_2_17_1","unstructured":"Mingsheng Long Han Zhu Jianmin Wang and Michael I Jordan. 2017. Deep Transfer Learning with Joint Adaptation Networks. In ICML.  Mingsheng Long Han Zhu Jianmin Wang and Michael I Jordan. 2017. Deep Transfer Learning with Joint Adaptation Networks. In ICML."},{"key":"e_1_3_2_2_18_1","volume-title":"Domain Adaptation: Learning Bounds and Algorithms. In COLT.","author":"Mansour Yishay","year":"2009","unstructured":"Yishay Mansour , Mehryar Mohri , and Afshin Rostamizadeh . 2009 . Domain Adaptation: Learning Bounds and Algorithms. In COLT. Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. 2009. Domain Adaptation: Learning Bounds and Algorithms. In COLT."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Shike Mei and Xiaojin Zhu. 2015. Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners.. In AAAI. 2871--2877.  Shike Mei and Xiaojin Zhu. 2015. Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners.. In AAAI. 2871--2877.","DOI":"10.1609\/aaai.v29i1.9569"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Luis Mu\u00f1oz-Gonz\u00e1lez Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil C Lupu and Fabio Roli. 2017. Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization. In AISec. 27--38.  Luis Mu\u00f1oz-Gonz\u00e1lez Battista Biggio Ambra Demontis Andrea Paudice Vasin Wongrassamee Emil C Lupu and Fabio Roli. 2017. Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization. In AISec. 27--38.","DOI":"10.1145\/3128572.3140451"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_2_22_1","unstructured":"Shahbaz Rezaei and Xin Liu. 2020. A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning. In ICLR.  Shahbaz Rezaei and Xin Liu. 2020. A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning. In ICLR."},{"key":"e_1_3_2_2_23_1","unstructured":"Ali Shafahi W Ronny Huang Mahyar Najibi Octavian Suciu Christoph Studer Tudor Dumitras and Tom Goldstein. 2018. Poison Frogs! Targeted Clean-label Poisoning Attacks on Neural Networks. In NeurIPS. 6103--6113.  Ali Shafahi W Ronny Huang Mahyar Najibi Octavian Suciu Christoph Studer Tudor Dumitras and Tom Goldstein. 2018. Poison Frogs! Targeted Clean-label Poisoning Attacks on Neural Networks. In NeurIPS. 6103--6113."},{"key":"e_1_3_2_2_24_1","volume-title":"Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein.","author":"Shafahi Ali","year":"2019","unstructured":"Ali Shafahi , Mahyar Najibi , Mohammad Amin Ghiasi , Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019 . Adversarial Training for Free!. In NeurIPS. 3358--3369. Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein. 2019. Adversarial Training for Free!. In NeurIPS. 3358--3369."},{"key":"e_1_3_2_2_25_1","unstructured":"Ali Shafahi Parsa Saadatpanah Chen Zhu Amin Ghiasi Christoph Studer David Jacobs and Tom Goldstein. 2020. Adversarially Robust Transfer Learning. In ICLR.  Ali Shafahi Parsa Saadatpanah Chen Zhu Amin Ghiasi Christoph Studer David Jacobs and Tom Goldstein. 2020. Adversarially Robust Transfer Learning. In ICLR."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Jian Shen Yanru Qu Weinan Zhang and Yong Yu. 2018. Wasserstein Distance Guided Representation Learning for Domain Adaptation. In AAAI.  Jian Shen Yanru Qu Weinan Zhang and Yong Yu. 2018. Wasserstein Distance Guided Representation Learning for Domain Adaptation. In AAAI.","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"e_1_3_2_2_27_1","unstructured":"Baochen Sun Jiashi Feng and Kate Saenko. 2016. Return of Frustratingly Easy Domain Adaptation. In AAAI.  Baochen Sun Jiashi Feng and Kate Saenko. 2016. Return of Frustratingly Easy Domain Adaptation. In AAAI."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Baochen Sun and Kate Saenko. 2016. Deep CORAL: Correlation Alignment for Deep Domain Adaptation. In ECCV. 443--450.  Baochen Sun and Kate Saenko. 2016. Deep CORAL: Correlation Alignment for Deep Domain Adaptation. In ECCV. 443--450.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"e_1_3_2_2_29_1","volume-title":"27th {USENIX} Security Symposium. 1281--1297.","author":"Wang Bolun","unstructured":"Bolun Wang , Yuanshun Yao , Bimal Viswanath , Haitao Zheng , and Ben Y Zhao . 2018. With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning . In 27th {USENIX} Security Symposium. 1281--1297. Bolun Wang, Yuanshun Yao, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2018. With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning. In 27th {USENIX} Security Symposium. 1281--1297."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Zirui Wang Zihang Dai Barnab\u00e1s P\u00f3czos and Jaime Carbonell. 2019. Characterizing and Avoiding Negative Transfer. In CVPR. 11293--11302.  Zirui Wang Zihang Dai Barnab\u00e1s P\u00f3czos and Jaime Carbonell. 2019. Characterizing and Avoiding Negative Transfer. In CVPR. 11293--11302.","DOI":"10.1109\/CVPR.2019.01155"},{"key":"e_1_3_2_2_31_1","volume-title":"Continuous Transfer Learning with Label-informed Distribution Alignment. arXiv preprint arXiv:2006.03230","author":"Wu Jun","year":"2020","unstructured":"Jun Wu and Jingrui He. 2020. Continuous Transfer Learning with Label-informed Distribution Alignment. arXiv preprint arXiv:2006.03230 ( 2020 ). Jun Wu and Jingrui He. 2020. Continuous Transfer Learning with Label-informed Distribution Alignment. arXiv preprint arXiv:2006.03230 (2020)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Ban Yikun Liu Xin Huang Ling Duan Yitao Liu Xue and Xu Wei. 2019. No place to hide: Catching fraudulent entities in tensors. In WWW. 83--93.  Ban Yikun Liu Xin Huang Ling Duan Yitao Liu Xue and Xu Wei. 2019. No place to hide: Catching fraudulent entities in tensors. In WWW. 83--93.","DOI":"10.1145\/3308558.3313403"},{"key":"e_1_3_2_2_33_1","unstructured":"Yuchen Zhang Tianle Liu Mingsheng Long and Michael Jordan. 2019. Bridging Theory and Algorithm for Domain Adaptation. In ICML. 7404--7413.  Yuchen Zhang Tianle Liu Mingsheng Long and Michael Jordan. 2019. Bridging Theory and Algorithm for Domain Adaptation. In ICML. 7404--7413."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Yinghua Zhang Yangqiu Song Jian Liang Kun Bai and Qiang Yang. 2020. Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning. In KDD. 2989--2997.  Yinghua Zhang Yangqiu Song Jian Liang Kun Bai and Qiang Yang. 2020. Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning. In KDD. 2989--2997.","DOI":"10.1145\/3394486.3403349"},{"key":"e_1_3_2_2_35_1","volume-title":"Kun Zhang, and Geoffrey Gordon.","author":"Zhao Han","year":"2019","unstructured":"Han Zhao , Remi Tachet Des Combes , Kun Zhang, and Geoffrey Gordon. 2019 . On Learning Invariant Representations for Domain Adaptation. In ICML. 7523--7532. Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon. 2019. On Learning Invariant Representations for Domain Adaptation. In ICML. 7523--7532."},{"key":"e_1_3_2_2_36_1","volume-title":"Deep Co-Attention Network for Multi-View Subspace Learning. arXiv preprint arXiv:2102.07751","author":"Zheng Lecheng","year":"2021","unstructured":"Lecheng Zheng , Yu Cheng , Hongxia Yang , Nan Cao , and Jingrui He. 2021. Deep Co-Attention Network for Multi-View Subspace Learning. arXiv preprint arXiv:2102.07751 ( 2021 ). Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, and Jingrui He. 2021. Deep Co-Attention Network for Multi-View Subspace Learning. arXiv preprint arXiv:2102.07751 (2021)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2019.00003"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Dawei Zhou Lecheng Zheng Yada Zhu Jianbo Li and Jingrui He. 2020. Domain Adaptive Multi-modality Neural Attention Network for Financial Forecasting. In WWW. 2230--2240.  Dawei Zhou Lecheng Zheng Yada Zhu Jianbo Li and Jingrui He. 2020. Domain Adaptive Multi-modality Neural Attention Network for Financial Forecasting. In WWW. 2230--2240.","DOI":"10.1145\/3366423.3380288"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467234"},{"key":"e_1_3_2_2_40_1","unstructured":"Chen Zhu W Ronny Huang Hengduo Li Gavin Taylor Christoph Studer and Tom Goldstein. 2019. Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. In ICML. 7614--7623  Chen Zhu W Ronny Huang Hengduo Li Gavin Taylor Christoph Studer and Tom Goldstein. 2019. Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. In ICML. 7614--7623"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467214","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:28Z","timestamp":1750191508000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":40,"alternative-id":["10.1145\/3447548.3467214","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467214","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}