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In this paper, we study the robustness of graph convolutional networks (GCNs).
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
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node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016
Aditya Grover and Jure Leskovec. 2016 · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2016 · 2016
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Adversarial Machine Learning at Scale
A. Kurakin, I. Goodfellow, and S. Bengio. 2016 · 2016
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Understanding Neural Networks through Representation Erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick D. McDaniel, and Ian J. Goodfellow. 2016a · 2016
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Crafting adversarial input sequences for recurrent neural networks. In 2016 IEEE Military Communications Conference, MILCOM 2016, Baltimore, MD, USA, November 1-3, 2016
Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, and Richard E. Harang. 2016b · 2016
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Column Networks for Collective Classification
T. Pham, T. Tran, D. Phung, and S. Venkatesh. 2016 · 2016
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On the Robustness of Semantic Segmentation Models to Adversarial Attacks
Anurag Arnab, Ondrej Miksik, and Philip H. S. Torr. 2017 · 2017
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Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, May 22-26, 2017
Nicholas Carlini and David A. Wagner. 2017 · 2017
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Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning
Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh. 2017a · 2017
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ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. In Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, AISec@CCS 2017, Dallas, TX, USA, November 3, 2017
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh. 2017b · 2017
Cited alongside, same era.
HotFlip: White-Box Adversarial Examples for NLP
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2017 · 2017
Cited alongside, same era.
Note on Attacking Object Detectors with Adversarial Stickers
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Dawn Song, Tadayoshi Kohno, Amir Rahmati, Atul Prakash, and Florian Tramèr. 2017 · 2017
Cited alongside, same era.
Inductive Representation Learning on Large Graphs
W. L. Hamilton, R. Ying, and J. Leskovec. 2017 · 2017
Cited alongside, same era.
Adversarial Examples for Semantic Segmentation and Object Detection. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan L. Yuille. 2017 · 2017
Later among the works it cites.
Can you fool AI with adversarial examples on a visual Turing test?
Xiaojun Xu, Xinyun Chen, Chang Liu, Anna Rohrbach, Trevor Darell, and Dawn Song. 2017 · 2017
Later among the works it cites.
Generating Natural Adversarial Examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh. 2017 · 2017
Later among the works it cites.
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Jie Chen, Tengfei Ma, and Cao Xiao. 2018 · 2018
Closest in time.
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
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Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2017 · 2017
Cited alongside, same era.
Adversarial Examples that Fool Detectors
Jiajun Lu, Hussein Sibai, and Evan Fabry. 2017 · 2017
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Cited alongside, same era.
Universal Adversarial Perturbations Against Semantic Image Segmentation. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, and Volker Fischer. 2017 · 2017
Cited alongside, same era.
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification
S. Rhee, S. Seo, and S. Kim. 2017 · 2017
Cited alongside, same era.
Towards Crafting Text Adversarial Samples
Suranjana Samanta and Sameep Mehta. 2017 · 2017
Cited alongside, same era.
Query-limited Black-box Attacks to Classifiers
Fnu Suya, Yuan Tian, David Evans, and Paolo Papotti. 2017 · 2017
Cited alongside, same era.
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick D. McDaniel. 2017 · 2017
Cited alongside, same era.
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh. 2018a · 2018
Closest in time.
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, and Cho-Jui Hsieh. 2018b · 2018
Closest in time.
Adversarial Attack on Graph Structured Data. In Proceedings of the 35th International Conference on Machine Learning
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018 · 2018
Closest in time.
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. In 2018 IEEE Security and Privacy Workshops, SP Workshops 2018, San Francisco, CA, USA, May 24, 2018
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
Closest in time.
Black-box Adversarial Attacks with Limited Queries and Information
A. Ilyas, L. Engstrom, A. Athalye, and J. Lin. 2018 · 2018
Closest in time.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018 · 2018
Closest in time.
Adversarial Attacks on Neural Networks for Graph Data
D. Zügner, A. Akbarnejad, and S. Günnemann. 2018 · 2018
Closest in time.
Adversarial Examples for Evaluating Reading Comprehension Systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017
Robin Jia and Percy Liang. 2017 · 2031
Closest in time.