Better constraints of imperceptibility, better adversarial examples in the text

[1]  Cho-Jui Hsieh,et al.  On the Robustness of Self-Attentive Models , 2019, ACL.

[2]  Hui Liu,et al.  Joint Character-Level Word Embedding and Adversarial Stability Training to Defend Adversarial Text , 2020, AAAI.

[3]  K. S. Rao,et al.  A novel approach to unsupervised pattern discovery in speech using Convolutional Neural Network , 2022, Comput. Speech Lang..

[4]  Dejing Dou,et al.  HotFlip: White-Box Adversarial Examples for Text Classification , 2017, ACL.

[5]  Hiroyuki Shindo,et al.  Interpretable Adversarial Perturbation in Input Embedding Space for Text , 2018, IJCAI.

[6]  Mani B. Srivastava,et al.  Generating Natural Language Adversarial Examples , 2018, EMNLP.

[7]  Zhifei Zhang,et al.  Feature Importance-aware Transferable Adversarial Attacks , 2021, ArXiv.

[8]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[9]  Luke S. Zettlemoyer,et al.  Adversarial Example Generation with Syntactically Controlled Paraphrase Networks , 2018, NAACL.

[10]  Michael I. Jordan,et al.  Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data , 2018, J. Mach. Learn. Res..

[11]  Vikram Pudi,et al.  Generating Natural Language Attacks in a Hard Label Black Box Setting , 2020, ArXiv.

[12]  Peter Szolovits,et al.  Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment , 2020, AAAI.

[13]  Zhiyuan Liu,et al.  Word-level Textual Adversarial Attacking as Combinatorial Optimization , 2019, ACL.

[14]  Shi Feng,et al.  Pathologies of Neural Models Make Interpretations Difficult , 2018, EMNLP.

[15]  Leonidas J. Guibas,et al.  A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[16]  Prashanth Vijayaraghavan,et al.  Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model , 2019, ECML/PKDD.

[17]  Ananthram Swami,et al.  Crafting adversarial input sequences for recurrent neural networks , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.

[18]  Percy Liang,et al.  Adversarial Examples for Evaluating Reading Comprehension Systems , 2017, EMNLP.

[19]  Shiguang Shan,et al.  Meta Gradient Adversarial Attack , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

[20]  Z. Anwar,et al.  CyberPulse++: A machine learning‐based security framework for detecting link flooding attacks in software defined networks , 2021, Int. J. Intell. Syst..

[21]  M. Ali Babar,et al.  ReinforceBug: A Framework to Generate Adversarial Textual Examples , 2021, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

[22]  Ali Farhadi,et al.  Bidirectional Attention Flow for Machine Comprehension , 2016, ICLR.

[23]  Baoyuan Wang,et al.  CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Xirong Li,et al.  Deep Text Classification Can be Fooled , 2017, IJCAI.

[25]  Bhuwan Dhingra,et al.  Combating Adversarial Misspellings with Robust Word Recognition , 2019, ACL.

[26]  Simon Burton,et al.  Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving , 2018, SAFECOMP.

[27]  Wanxiang Che,et al.  Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency , 2019, ACL.

[28]  Hwee Tou Ng,et al.  Improving the Robustness of Question Answering Systems to Question Paraphrasing , 2019, ACL.

[29]  Xinyu Dai,et al.  A Reinforced Generation of Adversarial Samples for Neural Machine Translation , 2019, ArXiv.

[30]  Ting Wang,et al.  TextBugger: Generating Adversarial Text Against Real-world Applications , 2018, NDSS.

[31]  Moustapha Cissé,et al.  Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples , 2017, NIPS.

[32]  Matteo Pagliardini,et al.  Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features , 2017, NAACL.

[33]  Qian Chen,et al.  T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack , 2020, EMNLP.

[34]  Simon See,et al.  ACT: an Attentive Convolutional Transformer for Efficient Text Classification , 2021, AAAI.

[35]  CNN-based intelligent safety surveillance in green IoT applications , 2021, China Communications.

[36]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[37]  Roger Wattenhofer,et al.  A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples , 2020, COLING.

[38]  Shruti Tople,et al.  To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers , 2020, ArXiv.

[39]  Yanjun Qi,et al.  Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[40]  Jonathan Berant,et al.  White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks , 2019, NAACL.

[41]  Kouichi Sakurai,et al.  One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.