Adv-OLM: Generating Textual Adversaries via OLM
Not on the map. Why?
Abstract (arXiv)
Deep learning models are susceptible to adversarial examples that have imperceptible perturbations in the original input, resulting in adversarial attacks against these models. Analysis of these attacks on the state of the art transformers in NLP can help improve the robustness of these models against such adversarial inputs. In this paper, we present Adv-OLM, a black-box attack method that adapts the idea of Occlusion and Language Models (OLM) to the current state of the art attack methods. OLM is used to rank words of a sentence, which are later substituted using word replacement strategies. We experimentally show that our approach outperforms other attack methods for several text classification tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 21 Jan 2021.
Builds on
21 of 30 references.
References · 21
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
- 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 709
- 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 568
- 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 488
- 2018Generating Natural Language Adversarial Examplescited by 422
- 2018TextBugger: Generating Adversarial Text Against Real-world Applicationscited by 376
- 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 341
9 more
- 2020BAE: BERT-based Adversarial Examples for Text Classificationcited by 316
- 2017Synthetic and Natural Noise Both Break Neural Machine Translationcited by 304
- 2019Certified Robustness to Adversarial Word Substitutionscited by 164
- 2019Combating Adversarial Misspellings with Robust Word Recognitioncited by 162
- 2018Pathologies of Neural Models Make Interpretations Difficultcited by 140
- 2018Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examplescited by 93
- 2020It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbationscited by 64
- 2019Word-level Textual Adversarial Attacking as Combinatorial Optimizationcited by 37
- 2020Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modelingcited by 11
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1.
2 papers.