A Complete List of All (arXiv) Adversarial Example Papers
[1] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow and Rob Fergus. Intriguing properties of neural networks. ICLR 2014.
[1] Ian J. Goodfellow, Jonathon Shlens and Christian Szegedy. Explaining and Harnessing Adversarial Examples. ICLR 2015.
[2] Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. Improving transferability of adversarial examples with input diversity. CVPR 2019.
[3] Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft. Nesterov Accelerated Gradient and Scale Invariance for Improving Transferability of Adversarial Examples. arXiv Preprint arXiv:1908.06281 2019.
[4] Lei Wu, Zhanxing Zhu, Cheng Tai and Weinan E. Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient. ICLR 2018 rejected.
[4] Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu and Jianguo Li. Boosting Adversarial Attacks with Momentum. CVPR 2018.
[1] Hyrum S. Anderson, Jonathan Woodbridge and Bobby Filar. DeepDGA: Adversarially-Tuned Domain Generation and Detection AISec 2016.
[2] Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu and Dawn Song. Generating Adversarial Examples with Adversarial Networks. IJCAI 2018.
[3] Yang Song, Rui Shu, Nate Kushman and Stefano Ermon. Constructing Unrestricted Adversarial Examples with Generative Models. NeurIPS 2018.
[4] Xiaosen Wang, Kun He and John E. Hopcroft. AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. arXiv Preprint arXiv:1904.07793 2019.
[5] Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen and Bo Li. AI-GAN: Attack-Inspired Generation of Adversarial Examples. arXiv Preprint arXiv:2002.02196 2020.
[1] Yang Song, Rui Shu, Nate Kushman and Stefano Ermon. Constructing Unrestricted Adversarial Examples with Generative Models. NeurIPS 2018.
[2] Xiaosen Wang, Kun He and John E. Hopcroft. AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. arXiv Preprint arXiv:1904.07793.
[1] Anh Nguyen, Jason Yosinski and Jeff Clune. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. CVPR 2015.
[1] Ian J. Goodfellow, Jonathon Shlens and Christian Szegedy. Explaining and Harnessing Adversarial Examples. ICLR 2015.
[2] Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft and Liwei Wang. Adversarially Robust Generalization Just Requires More Unlabeled Data arXiv Preprint arXiv:1906.00555.
[3] Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang and John C. Duchi. Unlabeled Data Improves Adversarial Robustness. arXiv Preprint arXiv:1905.13736.
[4] Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi and Pushmeet Kohli. Are Labels Required for Improving Adversarial Robustness?. arXiv Preprint arXiv:1905.13725.
[5] Chuanbiao Song, Kun He, Liwei Wang and John E. Hopcroft. Improving the Generalization of Adversarial Training with Domain Adaptation . ICLR 2019.
[1] Pouya Samangouei, Maya Kabkab and Rama Chellappa. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models. ICLR 2018.
[2] Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon and Nate Kushman. PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples ICLR 2018.
[3] Guoqing Jin, Shiwei Shen, Dongming Zhang, Feng Dai and Yongdong Zhang. APE-GAN: Adversarial Perturbation Elimination with GAN. ICASSP 2019.
[1] Eric Wong and J. Zico Kolter. Provable defenses against adversarial examples via the convex outer adversarial polytope. ICML 2017.
[2] Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann and Pushmeet Kohli. Scalable Verified Training for Provably Robust Image Classification. ICCV 2019.
[3] Jeremy M Cohen, Elan Rosenfeld and J. Zico Kolter. Certified Adversarial Robustness via Randomized Smoothing. ICML 2019.
[4] Guang-He Lee, Yang Yuan, Shiyu Chang and Tommi S. Jaakkola. Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. NeurIPS 2019.
[1] Matthew J. Roos. Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks. arXiv Preprint arXiv:2002.10084 2020.
[2] Alvin Chan, Yi Tay, Yew Soon Ong and Jie Fu. Jacobian Adversarially Regularized Networks for Robustness. ICLR 2020.
[3] Christian Etmann, Sebastian Lunz, Peter Maass and Carola-Bibiane Schönlieb. On the Connection Between Adversarial Robustness and Saliency Map Interpretability. ICML 2019.
[1] Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi and Chenliang Li. Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey. arXiv Preprint arXiv:1901.06796 2019.
[2] Wenqi Wang, Lina Wang, Benxiao Tang, Run Wang and Aoshuang Ye. Towards a Robust Deep Neural Network in Text Domain A Survey. arXiv Preprint arXiv:1902.07285 2019.
[1] Javid Ebrahimi, Anyi Rao, Daniel Lowd and Dejing Dou. HotFlip: White-Box Adversarial Examples for Text Classification. ACL 2018.
[2] Ji Gao, Jack Lanchantin, Mary Lou Soffa and Yanjun Qi. Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. IEEE S&P workshop 2018.
[1] Nicolas Papernot, Patrick McDaniel, Ananthram Swami and Richard Harang. Crafting Adversarial Input Sequences for Recurrent Neural Networks. MILCOM 2016.
[2] Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau and Stefano Ermon. Adversarial Examples for Natural Language Classification Problems . ICLR 2018 rejected.
[3] Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava and Kai-Wei Chang. Generating Natural Language Adversarial Examples. EMNLP 2018.
[4] Shuhuai Ren, Yihe Deng, Kun He and Wanxiang Che. Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency. ACL 2019.
[5] Huangzhao Zhang, Hao Zhou, Ning Miao and Lei Li. Generating Fluent Adversarial Examples for Natural Languages. ACL 2019.
[6] Yi-Ting Tsai, Min-Chu Yang and Han-Yu Chen. Adversarial Attack on Sentiment Classification. ACL workshop 2019.
[7] Samuel Barham and Soheil Feizi. Interpretable Adversarial Training for Text arXiv Preprint arXiv:1905.12864 2019.
[8] Di Jin, Zhijing Jin, Joey Tianyi Zhou and Peter Szolovits. Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment arXiv Preprint arXiv:1907.11932 2019.
[9] Xiaosen Wang, Hao Jin and Kun He. Natural Language Adversarial Attacks and Defenses in Word Level. arXiv Preprint arXiv:1909.06723 2019.
[1] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li and Wenchang Shi. Deep textclassification can be fooled. IJCAI 2018.
[2] Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li and Ting Wang. TextBugger: Generating Adversarial Text Against Real-world Applications. NDSS 2019.
[1] Di Li, Danilo Vasconcellos Vargas and Sakurai Kouichi. Universal Rules for Fooling Deep Neural Networks based Text Classification. CEC 2019.
[2] Melika Behjati, Seyed-Mohsen Moosavi-Dezfooli, Mahdieh Soleymani Baghshah and Pascal Frossard. Universal Adversarial Attacks on Text Classifiers. ICASSP 2019.
[1] Danish Pruthi, Bhuwan Dhingra and Zachary C. Lipton. Combating Adversarial Misspellings with Robust Word Recognition. ACL 2019.
[1] Ishai Rosenberg, Asaf Shabtai, Yuval Elovici and Lior Rokach. Defense Methods Against Adversarial Examples for Recurrent Neural Networks. arXiv Preprint arXiv:1901.09963 2019.
[2] Xiaosen Wang, Hao Jin and Kun He. Natural Language Adversarial Attacks and Defenses in Word Level. arXiv Preprint arXiv:1909.06723 2019.
[1] Nestor Rodriguez and Sergio Rojas-Galeano. Shielding Google's language toxicity model against adversarial attacks. arXiv Preprint arXiv:1801.01828 2018.
[2] Yichao Zhou, Jyun-Yu Jiang, Kai-Wei Chang and Wei Wang. Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification. EMNLP-IJCNLP 2019.
[1] Robin Jia, Aditi Raghunathan, Kerem Göksel and Percy Liang. Certified Robustness to Adversarial Word Substitutions. EMNLP-IJCNLP 2019.