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Must-read Papers on Textual Adversarial Attack and Defense
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README.md add one ACL-19 paper & update an ACL anthology link Aug 4, 2019

README.md

Must-read Papers on Textual Adversarial Attack and Defense (TAAD)

Contributed by Chenghao Yang, Fanchao Qi and Yuan Zang.

Content

Section Description
Survey Survey papers on Textual Attack and Defense
Black-box Only Attack generators only have access to confidence of victim models
White-box Only Attack generators have full access to victim models
Both Papers work on both black-box and white-box setting
Defense Only Papers work on defense
Evaluation Papers propose new evaluations of textual attacks and defense
Application of TAAD in Other Fields Papers apply TAAD in other fields except Natural Language Processing (NLP)

Survey Papers

  1. Analysis Methods in Neural Language Processing: A Survey. Yonatan Belinkov, James Glass. TACL 2019. [pdf]
  2. Towards a Robust Deep Neural Network in Text Domain A Survey. Wenqi Wang, Lina Wang, Benxiao Tang, Run Wang, Aoshuang Ye. 2019. [pdf]
  3. Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey. Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li. 2019. [pdf]

Black-box Only

  1. Probing Neural Network Understanding of Natural Language Arguments. Timothy Niven, Hung-Yu Kao. ACL 2019. [pdf] [code&data]
  2. PAWS: Paraphrase Adversaries from Word Scrambling. Yuan Zhang, Jason Baldridge, Luheng He. NAACL-HLT 2019. [pdf] [dataset]
  3. Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems. Steffen Eger, Gözde Gül ¸Sahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych. NAACL-HLT 2019. [pdf] [code&data]
  4. Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models. Tong Niu, Mohit Bansal. CoNLL 2018. [pdf] [code&data]
  5. Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge. Pasquale Minervini, Sebastian Riedel. CoNLL 2018. [pdf] [code&data]
  6. Generating Natural Language Adversarial Examples. Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, Kai-Wei Chang. EMNLP 2018. [pdf] [code]
  7. Breaking NLI Systems with Sentences that Require Simple Lexical Inferences. Max Glockner, Vered Shwartz, Yoav Goldberg ACL 2018. [pdf] [dataset]
  8. AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples. Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Eduard Hovy. ACL 2018. [pdf] [code]
  9. Semantically Equivalent Adversarial Rules for Debugging NLP Models. Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin ACL 2018. [pdf] [code]
  10. Robust Machine Comprehension Models via Adversarial Training. Yicheng Wang, Mohit Bansal. NAACL-HLT 2018. [pdf] [dataset]
  11. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer. NAACL-HLT 2018. [pdf] [code&data]
  12. Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers. Ji Gao, Jack Lanchantin, Mary Lou Soffa, Yanjun Qi. IEEE SPW 2018. [pdf] [code]
  13. Synthetic and Natural Noise Both Break Neural Machine Translation. Yonatan Belinkov, Yonatan Bisk. ICLR 2018. [pdf] [code&data]
  14. Generating Natural Adversarial Examples. Zhengli Zhao, Dheeru Dua, Sameer Singh. ICLR 2018. [pdf] [code]
  15. Adversarial Examples for Evaluating Reading Comprehension Systems. Robin Jia, and Percy Liang. EMNLP 2017. [pdf] [code]
  16. Adversarial Sets for Regularising Neural Link Predictors. Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel. UAI 2017 [pdf] [code]

White-box Only

  1. On Adversarial Examples for Character-Level Neural Machine Translation. Javid Ebrahimi, Daniel Lowd, Dejing Dou. COLING 2018. [pdf] [code]
  2. HotFlip: White-Box Adversarial Examples for Text Classification. Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou. ACL 2018. [pdf] [code]
  3. Towards Crafting Text Adversarial Samples. Suranjana Samanta, Sameep Mehta. ECIR 2018. [pdf]

Both

  1. Generating Fluent Adversarial Examples for Natural Languages. Huangzhao Zhang, Hao Zhou, Ning Miao, Lei Li. ACL 2019. [pdf]
  2. TEXTBUGGER: Generating Adversarial Text Against Real-world Applications. Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, Ting Wang. NDSS 2019. [pdf]
  3. Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension. Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, Ngoc Thang Vu. CoNLL 2018. [pdf]
  4. Deep Text Classification Can be Fooled. Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi. IJCAI 2018. [pdf]

Defense Only

  1. Combating Adversarial Misspellings with Robust Word Recognition. Danish Pruthi, Bhuwan Dhingra, Zachary C. Lipton. ACL 2019. [pdf] [code]

Evaluation

  1. On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models. Paul Michel, Xian Li, Graham Neubig, Juan Miguel Pino. NAACL-HLT 2019. [pdf] [code]

Application of TAAD in Other Fields

  1. Unified Visual-Semantic Embeddings: Bridging Vision and Language with Structured Meaning Representations. Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, Wei-Ying Ma. CVPR 2019. [pdf]
  2. Learning Visually-Grounded Semantics from Contrastive Adversarial Samples. Haoyue Shi, Jiayuan Mao, Tete Xiao, Yuning Jiang, Jian Sun. COLING 2018. [pdf] [code]
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