{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:43:00Z","timestamp":1783953780917,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009226","name":"National Security Agency","doi-asserted-by":"publisher","award":["H98230-21-1-0165, H98230-21-1-0278"],"award-info":[{"award-number":["H98230-21-1-0165, H98230-21-1-0278"]}],"id":[{"id":"10.13039\/100009226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2065"],"award-info":[{"award-number":["N00014-20-1-2065"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DoD Center of Excellence in AI and Machine Learning","award":["W911NF-20-2-0277"],"award-info":[{"award-number":["W911NF-20-2-0277"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2120279, CNS-1950704, CNS-1828593, and OAC-1829771"],"award-info":[{"award-number":["CNS-2120279, CNS-1950704, CNS-1828593, and OAC-1829771"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Commonwealth Cyber Initiative"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,27]]},"DOI":"10.1145\/3512527.3531373","type":"proceedings-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:23:32Z","timestamp":1656023012000},"page":"451-461","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Camouflaged Poisoning Attack on Graph Neural Networks"],"prefix":"10.1145","author":[{"given":"Chao","family":"Jiang","sequence":"first","affiliation":[{"name":"Auburn University, Auburn, AL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"He","sequence":"additional","affiliation":[{"name":"Old Dominion University, Norfolk, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Chapman","sequence":"additional","affiliation":[{"name":"Auburn University, Auburn, AL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyi","family":"Wu","sequence":"additional","affiliation":[{"name":"Old Dominion University, Norfolk, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2015.11.016"},{"key":"e_1_3_2_2_2_1","volume-title":"Curran Associates Inc.","author":"Atwood James","year":"2016","unstructured":"James Atwood and Don Towsley . 2016 . Diffusion-Convolutional Neural Networks (NeurIPS'16) . Curran Associates Inc. , Red Hook, NY, USA , 2001--2009. James Atwood and Don Towsley. 2016. Diffusion-Convolutional Neural Networks (NeurIPS'16). Curran Associates Inc., Red Hook, NY, USA, 2001--2009."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.006"},{"key":"e_1_3_2_2_4_1","volume-title":"Network propaganda: Manipulation, disinformation, and radicalization in American politics","author":"Benkler Yochai","unstructured":"Yochai Benkler , Robert Faris , and Hal Roberts . 2018. Network propaganda: Manipulation, disinformation, and radicalization in American politics . Oxford University Press . Yochai Benkler, Robert Faris, and Hal Roberts. 2018. Network propaganda: Manipulation, disinformation, and radicalization in American politics. Oxford University Press."},{"key":"e_1_3_2_2_5_1","unstructured":"Mustafa Bilgic Lilyana Mihalkova and Lise Getoor. 2010. Active learning for networked data. In ICML. 79--86.  Mustafa Bilgic Lilyana Mihalkova and Lise Getoor. 2010. Active learning for networked data. In ICML. 79--86."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3381028","article-title":"Outlier detection: Methods, models, and classification","volume":"53","author":"Boukerche Azzedine","year":"2020","unstructured":"Azzedine Boukerche , Lining Zheng , and Omar Alfandi . 2020 . Outlier detection: Methods, models, and classification . ACM Computing Surveys (CSUR) 53 , 3 (2020), 1 -- 37 . Azzedine Boukerche, Lining Zheng, and Omar Alfandi. 2020. Outlier detection: Methods, models, and classification. ACM Computing Surveys (CSUR) 53, 3 (2020), 1--37.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Adam Breuer Roee Eilat and Udi Weinsberg. 2020. Friend or faux: Graph-based early detection of fake accounts on social networks. In WWW. 1287--1297.  Adam Breuer Roee Eilat and Udi Weinsberg. 2020. Friend or faux: Graph-based early detection of fake accounts on social networks. In WWW. 1287--1297.","DOI":"10.1145\/3366423.3380204"},{"key":"e_1_3_2_2_8_1","volume-title":"ICLR2014","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann Lecun . 2014 . Spectral networks and locally connected networks on graphs . In ICLR2014 , CBLS, April 2014. Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun. 2014. Spectral networks and locally connected networks on graphs. In ICLR2014, CBLS, April 2014."},{"key":"e_1_3_2_2_9_1","volume-title":"Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society","author":"Carlini N.","year":"2017","unstructured":"N. Carlini and D. Wagner . 2017 . Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society , Los Alamitos, CA, USA, 39--57. https:\/\/doi.org\/10.1109\/SP. 2017 .49 10.1109\/SP.2017.49 N. Carlini and D. Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, Los Alamitos, CA, USA, 39--57. https:\/\/doi.org\/10.1109\/SP.2017.49"},{"key":"e_1_3_2_2_10_1","unstructured":"Jianbo Chen Le Song Martin Wainwright and Michael Jordan. 2018. Learning to explain: An information-theoretic perspective on model interpretation. In ICML. PMLR 883--892.  Jianbo Chen Le Song Martin Wainwright and Michael Jordan. 2018. Learning to explain: An information-theoretic perspective on model interpretation. In ICML. PMLR 883--892."},{"key":"e_1_3_2_2_11_1","unstructured":"Piotr Dabkowski and Yarin Gal. 2017. Real time image saliency for black box classifiers. In NeurIPS.  Piotr Dabkowski and Yarin Gal. 2017. Real time image saliency for black box classifiers. In NeurIPS."},{"key":"e_1_3_2_2_12_1","unstructured":"Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial attack on graph structured data. In ICML. PMLR 1115--1124.  Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial attack on graph structured data. In ICML. PMLR 1115--1124."},{"key":"e_1_3_2_2_13_1","unstructured":"Hao-Wen Dong and Yi-Hsuan Yang. 2018. Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation. arXiv:1810.04714 [cs.LG]  Hao-Wen Dong and Yi-Hsuan Yang. 2018. Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation. arXiv:1810.04714 [cs.LG]"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW. 417--426.  Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_15_1","unstructured":"Hongyang Gao Zhengyang Wang and Shuiwang Ji. 2018. Large-scale learnable graph convolutional networks. In KDD. 1416--1424.  Hongyang Gao Zhengyang Wang and Shuiwang Ji. 2018. Large-scale learnable graph convolutional networks. In KDD. 1416--1424."},{"key":"e_1_3_2_2_16_1","volume-title":"Robustness of Graph Neural Networks at Scale. NeurIPS 34","author":"Geisler Simon","year":"2021","unstructured":"Simon Geisler , Tobias Schmidt , Hakan Sirin , Daniel Z\u00fcgner , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2021. Robustness of Graph Neural Networks at Scale. NeurIPS 34 ( 2021 ). Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Z\u00fcgner, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2021. Robustness of Graph Neural Networks at Scale. NeurIPS 34 (2021)."},{"key":"e_1_3_2_2_17_1","volume-title":"Explaining and Harnessing Adversarial Examples. CoRR abs\/1412.6572","author":"Goodfellow Ian J.","year":"2015","unstructured":"Ian J. Goodfellow , Jonathon Shlens , and Christian Szegedy . 2015. Explaining and Harnessing Adversarial Examples. CoRR abs\/1412.6572 ( 2015 ). Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and Harnessing Adversarial Examples. CoRR abs\/1412.6572 (2015)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"e_1_3_2_2_19_1","volume-title":"Deep matrix tri-factorization: Mining vertex-wise interactions in multi-space attributed graphs","author":"He Yi","unstructured":"Yi He , Sheng Chen , Thu Nguyen , Bruce A Wade , and Xindong Wu. 2020. Deep matrix tri-factorization: Mining vertex-wise interactions in multi-space attributed graphs . In SDM. SIAM , 334--342. Yi He, Sheng Chen, Thu Nguyen, Bruce A Wade, and Xindong Wu. 2020. Deep matrix tri-factorization: Mining vertex-wise interactions in multi-space attributed graphs. In SDM. SIAM, 334--342."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Yi He Sheng Chen Baijun Wu Xu Yuan and Xindong Wu. 2021. Unsupervised Lifelong Learning with Curricula. In WWW. 3534--3545.  Yi He Sheng Chen Baijun Wu Xu Yuan and Xindong Wu. 2021. Unsupervised Lifelong Learning with Curricula. In WWW. 3534--3545.","DOI":"10.1145\/3442381.3449839"},{"key":"e_1_3_2_2_21_1","volume-title":"Active Learning with Multi-Granular Graph Auto-Encoder","author":"He Yi","unstructured":"Yi He , Xu Yuan , Nian-Feng Tzeng , and Xindong Wu. 2020. Active Learning with Multi-Granular Graph Auto-Encoder . In ICDM. IEEE , 1058--1063. Yi He, Xu Yuan, Nian-Feng Tzeng, and Xindong Wu. 2020. Active Learning with Multi-Granular Graph Auto-Encoder. In ICDM. IEEE, 1058--1063."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Wei Jin Yao Ma Xiaorui Liu Xianfeng Tang Suhang Wang and Jiliang Tang. 2020. Graph structure learning for robust graph neural networks. In KDD. 66--74.  Wei Jin Yao Ma Xiaorui Liu Xianfeng Tang Suhang Wang and Jiliang Tang. 2020. Graph structure learning for robust graph neural networks. In KDD. 66--74.","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_2_23_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.08.019"},{"key":"e_1_3_2_2_25_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_2_26_1","doi-asserted-by":"crossref","unstructured":"Jia Li Honglei Zhang Zhichao Han Yu Rong Hong Cheng and Junzhou Huang. 2020. Adversarial attack on community detection by hiding individuals. In WWW. 917--927.  Jia Li Honglei Zhang Zhichao Han Yu Rong Hong Cheng and Junzhou Huang. 2020. Adversarial attack on community detection by hiding individuals. In WWW. 917--927.","DOI":"10.1145\/3366423.3380171"},{"key":"e_1_3_2_2_27_1","volume-title":"COPOD: Copula-Based Outlier Detection","author":"Li Zheng","year":"2020","unstructured":"Zheng Li , Yue Zhao , Nicola Botta , Cezar Ionescu , and Xiyang Hu . 2020 . COPOD: Copula-Based Outlier Detection . In ICDM. IEEE. Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu. 2020. COPOD: Copula-Based Outlier Detection. In ICDM. IEEE."},{"key":"e_1_3_2_2_28_1","unstructured":"Wenjuan Luo Han Zhang Xiaodi Yang Lin Bo Xiaoqing Yang Zang Li Xiaohu Qie and Jieping Ye. 2020. Dynamic heterogeneous graph neural network for real-time event prediction. In KDD. 3213--3223.  Wenjuan Luo Han Zhang Xiaodi Yang Lin Bo Xiaoqing Yang Zang Li Xiaohu Qie and Jieping Ye. 2020. Dynamic heterogeneous graph neural network for real-time event prediction. In KDD. 3213--3223."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Julian McAuley Christopher Targett Qinfeng Shi and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR. 43--52.  Julian McAuley Christopher Targett Qinfeng Shi and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR. 43--52.","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_2_30_1","volume-title":"Collective classification in network data. AI magazine 29, 3","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Galligher , and Tina Eliassi-Rad . 2008. Collective classification in network data. AI magazine 29, 3 ( 2008 ), 93--93. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17147"},{"key":"e_1_3_2_2_32_1","volume-title":"GAEN: Graph Attention Evolving Networks","author":"Shi Min","year":"2021","unstructured":"Min Shi , Yu Huang , Xingquan Zhu , Yufei Tang , Yuan Zhuang , and Jianxun Liu . 2021 . GAEN: Graph Attention Evolving Networks . In IJCAI, Zhi-Hua Zhou (Ed.). ijcai.org, 1541--1547. https:\/\/doi.org\/10.24963\/ijcai.2021\/213 10.24963\/ijcai.2021 Min Shi, Yu Huang, Xingquan Zhu, Yufei Tang, Yuan Zhuang, and Jianxun Liu. 2021. GAEN: Graph Attention Evolving Networks. In IJCAI, Zhi-Hua Zhou (Ed.). ijcai.org, 1541--1547. https:\/\/doi.org\/10.24963\/ijcai.2021\/213"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Yiwei Sun Suhang Wang Xianfeng Tang Tsung-Yu Hsieh and Vasant Honavar. 2020. Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. In WWW. 673--683.  Yiwei Sun Suhang Wang Xianfeng Tang Tsung-Yu Hsieh and Vasant Honavar. 2020. Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. In WWW. 673--683.","DOI":"10.1145\/3366423.3380149"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380149"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006004"},{"key":"e_1_3_2_2_36_1","volume-title":"ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units using Chebyshev Approximations. CoRR abs\/1911.05467","author":"Tang Shanshan","year":"2019","unstructured":"Shanshan Tang , Bo Li , and Haijun Yu. 2019. ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units using Chebyshev Approximations. CoRR abs\/1911.05467 ( 2019 ). Shanshan Tang, Bo Li, and Haijun Yu. 2019. ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units using Chebyshev Approximations. CoRR abs\/1911.05467 (2019)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Xianfeng Tang Yandong Li Yiwei Sun Huaxiu Yao Prasenjit Mitra and Suhang Wang. 2020. Transferring robustness for graph neural network against poisoning attacks. In WSDM. 600--608.  Xianfeng Tang Yandong Li Yiwei Sun Huaxiu Yao Prasenjit Mitra and Suhang Wang. 2020. Transferring robustness for graph neural network against poisoning attacks. In WSDM. 600--608.","DOI":"10.1145\/3336191.3371851"},{"key":"e_1_3_2_2_38_1","unstructured":"Shuchang Tao Qi Cao Huawei Shen Junjie Huang Yunfan Wu and Xueqi Cheng. 2021. Single Node Injection Attack against Graph Neural Networks. In CIKM. 1794--1803.  Shuchang Tao Qi Cao Huawei Shen Junjie Huang Yunfan Wu and Xueqi Cheng. 2021. Single Node Injection Attack against Graph Neural Networks. In CIKM. 1794--1803."},{"key":"e_1_3_2_2_39_1","volume-title":"Graph Attention Networks. ICLR","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018. Graph Attention Networks. ICLR ( 2018 ). https:\/\/openreview.net\/forum?id=rJXMpikCZ Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. ICLR (2018). https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"e_1_3_2_2_40_1","volume-title":"Neil Zhenqiang Gong, and Hao Fu","author":"Wang Binghui","year":"2017","unstructured":"Binghui Wang , Neil Zhenqiang Gong, and Hao Fu . 2017 . GANG : Detecting fraudulent users in online social networks via guilt-by-association on directed graphs. In ICDM. IEEE , 465--474. Binghui Wang, Neil Zhenqiang Gong, and Hao Fu. 2017. GANG: Detecting fraudulent users in online social networks via guilt-by-association on directed graphs. In ICDM. IEEE, 465--474."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Binghui Wang Jinyuan Jia Xiaoyu Cao and Neil Zhenqiang Gong. 2021. Certified robustness of graph neural networks against adversarial structural perturbation. In KDD. 1645--1653.  Binghui Wang Jinyuan Jia Xiaoyu Cao and Neil Zhenqiang Gong. 2021. Certified robustness of graph neural networks against adversarial structural perturbation. In KDD. 1645--1653.","DOI":"10.1145\/3447548.3467295"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00070"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","unstructured":"Hongwei Wang Fuzheng Zhang Mengdi Zhang Jure Leskovec Miao Zhao Wenjie Li and Zhongyuan Wang. 2019. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In KDD. 968--977.  Hongwei Wang Fuzheng Zhang Mengdi Zhang Jure Leskovec Miao Zhao Wenjie Li and Zhongyuan Wang. 2019. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In KDD. 968--977.","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00696-7"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2019.2961895"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3014302"},{"key":"e_1_3_2_2_47_1","volume-title":"A deep latent factor model for high-dimensional and sparse matrices in recommender systems","author":"Wu Di","year":"2019","unstructured":"Di Wu , Xin Luo , Mingsheng Shang , Yi He , Guoyin Wang , and MengChu Zhou . 2019. A deep latent factor model for high-dimensional and sparse matrices in recommender systems . IEEE Transactions on Systems, Man, and Cybernetics: Systems ( 2019 ). Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and MengChu Zhou. 2019. A deep latent factor model for high-dimensional and sparse matrices in recommender systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019)."},{"key":"e_1_3_2_2_48_1","unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. PMLR 6861--6871.  Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. PMLR 6861--6871."},{"key":"e_1_3_2_2_49_1","unstructured":"Huijun Wu Chen Wang Yuriy Tyshetskiy Andrew Docherty Kai Lu and Liming Zhu. 2019. Adversarial examples on graph data: Deep insights into attack and defense. In IJCAI.  Huijun Wu Chen Wang Yuriy Tyshetskiy Andrew Docherty Kai Lu and Liming Zhu. 2019. Adversarial examples on graph data: Deep insights into attack and defense. In IJCAI."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1361684.1361686"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v36i3.2601"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_53_1","volume-title":"universal, and robust adversarial attacks against speaker recognition systems","author":"Xie Yi","unstructured":"Yi Xie , Cong Shi , Zhuohang Li , Jian Liu , Yingying Chen , and Bo Yuan . 2020. Real-time , universal, and robust adversarial attacks against speaker recognition systems . In ICASSP. IEEE , 1738--1742. Yi Xie, Cong Shi, Zhuohang Li, Jian Liu, Yingying Chen, and Bo Yuan. 2020. Real-time, universal, and robust adversarial attacks against speaker recognition systems. In ICASSP. IEEE, 1738--1742."},{"key":"e_1_3_2_2_54_1","unstructured":"Haitao Xu Daiping Liu Haining Wang and Angelos Stavrou. 2015. E-commerce reputation manipulation: The emergence of reputation-escalation-as-a-service. In WWW. 1296--1306.  Haitao Xu Daiping Liu Haining Wang and Angelos Stavrou. 2015. E-commerce reputation manipulation: The emergence of reputation-escalation-as-a-service. In WWW. 1296--1306."},{"key":"e_1_3_2_2_55_1","unstructured":"Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong and Xue Lin. 2019. Topology attack and defense for graph neural networks: An optimization perspective. In IJCAI.  Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong and Xue Lin. 2019. Topology attack and defense for graph neural networks: An optimization perspective. In IJCAI."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09717-4"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3374217","article-title":"Adversarial attacks on deep-learning models in natural language processing: A survey","volume":"11","author":"Zhang Wei Emma","year":"2020","unstructured":"Wei Emma Zhang , Quan Z Sheng , Ahoud Alhazmi , and Chenliang Li . 2020 . Adversarial attacks on deep-learning models in natural language processing: A survey . ACM Transactions on Intelligent Systems and Technology 11 , 3 (2020), 1 -- 41 . Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li. 2020. Adversarial attacks on deep-learning models in natural language processing: A survey. ACM Transactions on Intelligent Systems and Technology 11, 3 (2020), 1--41.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_2_58_1","volume-title":"Gnnguard: Defending graph neural networks against adversarial attacks. In NeurIPS.","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik . 2020 . Gnnguard: Defending graph neural networks against adversarial attacks. In NeurIPS. Xiang Zhang and Marinka Zitnik. 2020. Gnnguard: Defending graph neural networks against adversarial attacks. In NeurIPS."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"crossref","unstructured":"Shuai Zheng Zhenfeng Zhu Xingxing Zhang Zhizhe Liu Jian Cheng and Yao Zhao. 2020. Distribution-induced bidirectional generative adversarial network for graph representation learning. In CVPR. 7224--7233.  Shuai Zheng Zhenfeng Zhu Xingxing Zhang Zhizhe Liu Jian Cheng and Yao Zhao. 2020. Distribution-induced bidirectional generative adversarial network for graph representation learning. In CVPR. 7224--7233.","DOI":"10.1109\/CVPR42600.2020.00725"},{"key":"e_1_3_2_2_60_1","unstructured":"Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust graph convolutional networks against adversarial attacks. In KDD. 1399--1407.  Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust graph convolutional networks against adversarial attacks. In KDD. 1399--1407."},{"key":"e_1_3_2_2_61_1","volume-title":"TDGIA: Effective Injection Attacks on Graph Neural Networks. In KDD","author":"Zou Xu","year":"2021","unstructured":"Xu Zou , Qinkai Zheng , Yuxiao Dong , Xinyu Guan , Evgeny Kharlamov , Jialiang Lu , and Jie Tang . 2021 . TDGIA: Effective Injection Attacks on Graph Neural Networks. In KDD , Vol. abs\/ 2106 .06663. Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang. 2021. TDGIA: Effective Injection Attacks on Graph Neural Networks. In KDD, Vol. abs\/2106.06663."},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial attacks on graph neural networks via meta learning. In ICLR.  Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial attacks on graph neural networks via meta learning. In ICLR.","DOI":"10.24963\/ijcai.2019\/872"},{"key":"#cr-split#-e_1_3_2_2_63_1.1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial Attacks on Neural Networks for Graph Data. In KDD. ACM. https:\/\/doi.org\/10.1145\/3219819.3220078 10.1145\/3219819.3220078","DOI":"10.1145\/3219819.3220078"},{"key":"#cr-split#-e_1_3_2_2_63_1.2","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial Attacks on Neural Networks for Graph Data. In KDD. ACM. https:\/\/doi.org\/10.1145\/3219819.3220078","DOI":"10.24963\/ijcai.2019\/872"}],"event":{"name":"ICMR '22: International Conference on Multimedia Retrieval","location":"Newark NJ USA","acronym":"ICMR '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 2022 International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512527.3531373","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3512527.3531373","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3512527.3531373","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:12Z","timestamp":1750188612000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3512527.3531373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,27]]},"references-count":64,"alternative-id":["10.1145\/3512527.3531373","10.1145\/3512527"],"URL":"https:\/\/doi.org\/10.1145\/3512527.3531373","relation":{},"subject":[],"published":{"date-parts":[[2022,6,27]]},"assertion":[{"value":"2022-06-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}