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Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing (NLP)).
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W. E. Zhang, Q. Z. Sheng, A. Alhazmi, and C. Li, “Adversarial attacks on deep learning models in natural language processing: A survey,” ACM Transactions on Intelligent Systems and Technology , vol. 11, no. 3, 2020
2020
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W. Zhong, D. Tang, Z. Xu, R. Wang, N. Duan, M. Zhou, J. Wang, and J. Yin, “Neural deepfake detection with factual structure of text,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020
2020
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Y. Wang, W. Yang, F. Ma, J. Xu, B. Zhong, Q. Deng, and J. Gao, “Weak supervision for fake news detection via reinforcement learning,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , 2020
2020
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D. Jin, Z. Jin, J. T. Zhou, and P. Szolovits, “Is bert really robust? a strong baseline for natural language attack on text classification and entailment,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , 2020
2020
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L. Li, R. Ma, Q. Guo, X. Xue, and X. Qiu, “Bert-attack: Adversarial attack against bert using bert,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , 2020
2020
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S. Garg and G. Ramakrishnan, “Bae: Bert-based adversarial examples for text classification,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , 2020
2020
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P. Yang, J. Chen, C.-J. Hsieh, Jane-LingWang, and M. I. Jordan, “Greedy attack and gumbel attack: Generating adversarial examples for discrete data,” Journal of Machine Learning Research , vol. 21, pp. 1–36, 2020
2020
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B. Wang, H. Pei, B. Pan, Q. Chen, S. Wang, and B. Li, “T3: Tree-autoencoder constrained adversarial text generation for targeted attack,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , 2020
2020
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Y. Zang, F. Qi, C. Yang, Z. Liu, M. Zhang, Q. Liu, and M. Sun, “Word-level textual adversarial attacking as combinatorial optimization,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . ACL, 2020
2020
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S. Tan, S. Joty, M.-Y. Kan, and R. Socher, “It’s morphin’ time! combating linguistic discrimination with inflectional perturbations,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . ACL, 2020, p. 2920–2935
2020
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W. Zou, S. Huang, J. Xie, X. Dai, and J. Chen, “A reinforced generation of adversarial examples for neural machine translation,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . ACL, 2020, p. 3486–3497
2020
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G. Ian, P.-A. Jean, M. Mehdi, X. Bing, W.-F. David, O. Sherjil, C. Aaron, and B. Yoshua, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, p. 139–144, 2020
2020
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B. Wang, B. Pan, X. Li, and B. Li, “Towards evaluating the robustness of chinese bert classifiers,” in arXiv preprint arXiv: 2004.03742 , 2020
2020
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M. Cheng, J. Yi, P.-Y. Chen, H. Zhang, and C.-J. Hsieh, “Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , 2020
2020
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X. Zheng, J. Zeng, Y. Zhou, C.-J. Hsieh, M. Cheng, and X. Huang, “Evaluating and enhancing the robustness of neural network-based dependency parsing models with adversarial examples,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , 2020, p. 6600–6610
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L. Li, Y. Shao, D. Song, X. Qiu, and X. Huang, “Generating adversarial examples in chinese texts using sentence-pieces,” in arXiv preprint arXiv: 2012.14769 , 2020
2020
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E. Jones, R. Jia, A. Raghunathan, and P. Liang, “Robust encodings: A framework for combating adversarial typos,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . ACL, 2020, p. 2752–2765
2020
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Z. Wang and H. Wang, “Defense of word-level adversarial attacks via random substitution encoding,” in Proceedings of the International Conference on Knowledge Science, Engineering and Management , 2020
2020
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K. Liu, X. Liu, A. Yang, J. Liu, J. Su, S. Li, and Q. She, “A robust adversarial training approach to machine reading comprehension,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , 2020
2020
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H. Liu, Y. Zhang, Y. Wang, Z. Lin, and Y. Chen, “Joint character-level word embedding and adversarial stability training to defend adversarial text,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , 2020
2020
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Z. Shi, H. Zhang, K.-W. Chang, M. Huang, and C.-J. Hsieh, “Robustness verification for transformers,” in Proceedings of the International Conference on Learning Representations , 2020
2020
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M. Ye, C. Gong, and Q. Liu, “Safer: A structure-free approach for certified robustness to adversarial word substitutions,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . ACL, 2020, p. 3465–3475
2020
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Y. Sun, S. Wang, Y. Li, S. Feng, H. Tian, H. Wu, and H. Wang, “Ernie 2.0: A continual pre-training framework for language understanding,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , 2020
2020
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J. Li, T. Du, S. Ji, R. Zhang, Q. Lu, M. Yang, and T. Wang, “Textshield: Robust text classification based on multimodal embedding and neural machine translation,” in Proceedings of the 29th USENIX Security Symposium (USENIX Security 20) . USENIX Association, 2020, pp. 1381–1398
2020
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S. Sharmin, N. Rathi, P. Panda, and K. Roy, “Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations,” in Proceedings of the 16th European Conference on Computer Vision (ECCV 2020) , 2020
2020
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J. X. Morris, E. Lifland, J. Y. Yoo, J. Grigsby, D. Jin, and Y. Qi, “Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp,” in arXiv preprint arXiv: 2005.05909 , 2020
2020
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F. Suya, J. Chi, D. Evans, and Y. Tian, “Hybrid batch attacks: Finding black-box adversarial examples with limited queries,” in Proceedings of the 29th USENIX Security Symposium , 2020
2020
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R. Wang, F. Juefei-Xu, L. Ma, X. Xie, Y. Huang, J. Wang, and Y. Liu, “Fakespotter: A simple yet robust baseline for spotting ai-synthesized fake faces,” in Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020) , 2020
2020
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S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, “Cnn-generated images are surprisingly easy to spot… for now,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
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A. Gandhi and S. Jain, “Adversarial perturbations fool deepfake detectors,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN 2020) , 2020
2020
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N. Carlini and H. Farid, “Evading deepfake-image detectors with white- and black-box attacks,” in Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2020
2020
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N. Ruiz, S. A. Bargal, and S. Sclaroff, “Disrupting deepfakes: Adversarial attacks against conditional image translation networks and facial manipulation systems,” in Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2020
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P. Neekhara, B. Dolhansky, J. Bitton, and C. C. Ferrer, “Adversarial threats to deepfake detection: A practical perspective,” in arXiv preprint arXiv: 2011.09957 , 2020
2020
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R. Maheshwary, S. Maheshwary, and V. Pudi, “Generating natural language attacks in a hard label black box setting,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence , 2021
2021
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C. Zhang, A. Liu, X. Liu, Y. Xu, H. Yu, Y. Ma, and T. Li, “Interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity,” IEEE Transactions on Image Processing , vol. 30, pp. 1291–1304, 2021
2021
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H. Zhang, Y. Avrithis, T. Furon, and L. Amsaleg, “Walking on the edge: Fast, low-distortion adversarial examples,” IEEE Transactions on Information Forensics and Security , vol. 16, pp. 701–713, 2021
2021
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P. Neekhara, S. Hussain, M. Jere, F. Koushanfar, and J. McAuley, “Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples,” in Proceedings of the International Workshop on Applications of Computer Vision , 2021
2021
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R. Jia and P. Liang, “Adversarial examples for evaluating reading comprehension systems,” in Proceedings of the 2017 conference on empirical methods in natural language processing (EMNLP) , 2017, p. 2021–2031
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M. Aubakirova and M. Bansal, “Interpreting neural networks to improve politeness comprehension,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 2016, p. 2035–2041
2041
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