mlsanityData to 7 Oct 2026

Towards Effective Paraphrasing for Information Disguise

arXiv 2311.05018 ↗First posted 8 Nov 2023cs.IR cs.AIcited by 5

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Abstract (arXiv)

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet. Research on ID becomes important when authors' written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or their proxies, neural retriever (NeurIR) models. We propose a framework where, for a given sentence from an author's post, we perform iterative perturbation on the sentence in the direction of paraphrasing with an attempt to confuse the search mechanism of a NeurIR system when the sentence is queried on it. Our experiments involve the subreddit 'r/AmItheAsshole' as the source of public content and Dense Passage Retriever as a NeurIR system-based proxy for search engines. Our work introduces a novel method of phrase-importance rankings using perplexity scores and involves multi-level phrase substitutions via beam search. Our multi-phrase substitution scheme succeeds in disguising sentences 82% of the time and hence takes an essential step towards enabling researchers to disguise sensitive content effectively before making it public. We also release the code of our approach.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

21 of 31 references.

References · 21

  1. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  2. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  3. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 663
  4. 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 488
  5. 2018Universal Sentence Encodercited by 485
  6. 2018Generating Natural Language Adversarial Examplescited by 422
  7. 2019MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distancecited by 415
  8. 2018TextBugger: Generating Adversarial Text Against Real-world Applicationscited by 376
  9. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 341
  10. 2020BAE: BERT-based Adversarial Examples for Text Classificationcited by 316
  11. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 282
  12. 2019Certified Robustness to Adversarial Word Substitutionscited by 164
9 more
  1. 2016Counter-fitting Word Vectors to Linguistic Constraintscited by 138
  2. 2018Multilingual Constituency Parsing with Self-Attention and Pre-Trainingcited by 117
  3. 2021Towards Improving Adversarial Training of NLP Modelscited by 79
  4. 2018Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledgecited by 45
  5. 2022PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Modelscited by 44
  6. 2020One word at a time: adversarial attacks on retrieval modelscited by 23
  7. 2022BERT Rankers are Brittle: a Study using Adversarial Document Perturbationscited by 16
  8. 2018D-PAGE: Diverse Paraphrase Generationcited by 7
  9. 2019The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correctioncited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 2.

5 papers.

  1. 2024WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensionscited by 10
  2. 2024Measuring Moral Inconsistencies in Large Language Modelscited by 3
  3. 2024SaGE: Evaluating Moral Consistency in Large Language Modelscited by 3
  4. 2026QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregationcited by 0
  5. 2026Constrained Paraphrase Consistency for LLM Hallucination Detectioncited by 0