Summary of Semantic Stealth: Adversarial Text Attacks on Nlp Using Several Methods, by Roopkatha Dey et al.
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods
by Roopkatha Dey, Aivy Debnath, Sayak Kumar Dutta, Kaustav Ghosh, Arijit Mitra, Arghya Roy Chowdhury, Jaydip Sen
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recently developed natural language processing (NLP) model called BERT has shown impressive performance in various tasks such as machine translation, sentiment analysis, and question answering. However, researchers have identified a significant vulnerability in these models’ robustness to text adversarial attacks. These attacks involve manipulating input text to mislead the model’s predictions while maintaining human interpretability. Despite BERT’s remarkable performance, it remains vulnerable to adversarial perturbations in the input text. To address this vulnerability, three distinct attack mechanisms are explored using BERT as the victim model: BERT-on-BERT attack, PWWS attack, and Fraud Bargain’s Attack (FBA). The attacks are tested on the IMDB, AG News, and SST2 datasets to assess their effectiveness against the BERT classifier model. The results show that PWWS emerges as the most potent adversary, consistently outperforming other methods across multiple evaluation scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Natural language processing models like BERT help us communicate efficiently in various areas such as healthcare and finance. However, these models can be tricked by manipulating input text to make wrong predictions. Researchers are trying to find ways to make these models more robust against these attacks. In this paper, the authors explore three different methods that can fool the BERT model: BERT-on-BERT attack, PWWS attack, and Fraud Bargain’s Attack (FBA). They tested these attacks on several datasets to see which one is most effective. The results show that one method, called PWWS, is particularly good at generating misleading text examples. |
Keywords
» Artificial intelligence » Bert » Natural language processing » Nlp » Question answering » Translation
