Abstract
We introduce a simple yet efficient sentence-level attack on black-box toxicity detector models. By adding several positive words or sentences to the end of a hateful message, we are able to change the prediction of a neural network and pass the toxicity detection system check. This approach is shown to be working on seven languages from three different language families. We also describe the defence mechanism against the aforementioned attack and discuss its limitations.- Anthology ID:
- 2023.findings-emnlp.155
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2362–2369
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.155
- DOI:
- 10.18653/v1/2023.findings-emnlp.155
- Cite (ACL):
- Sergey Berezin, Reza Farahbakhsh, and Noel Crespi. 2023. No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2362–2369, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks (Berezin et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2023.findings-emnlp.155.pdf