XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks
Tom Roth, Inigo Jauregi Unanue, Alsharif Abuadbba, Massimo Piccardi
Abstract
An adversarial attack to a text classifier consists of an input that induces the classifier into an incorrect class prediction, while retaining all the linguistic properties of correctly-classified examples. A popular class of adversarial attacks exploits the gradients of the victim classifier to train a dedicated generative model to produce effective adversarial examples. However, this training signal alone is not sufficient to ensure other desirable properties of the adversarial attacks, such as similarity to non-adversarial examples, linguistic fluency, grammaticality, and so forth. For this reason, in this paper we propose a novel training objective which leverages a set of pretrained language models to promote such properties in the adversarial generation. A core component of our approach is a set of vocabulary-mapping matrices which allow cascading the generative model to any victim or component model of choice, while retaining differentiability end-to-end. The proposed approach has been tested in an ample set of experiments covering six text classification datasets, two victim models, and four baselines. The results show that it has been able to produce effective adversarial attacks, outperforming the compared generative approaches in a majority of cases and proving highly competitive against established token-replacement approaches.- Anthology ID:
- 2024.lrec-main.1544
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 17753–17763
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1544
- DOI:
- Cite (ACL):
- Tom Roth, Inigo Jauregi Unanue, Alsharif Abuadbba, and Massimo Piccardi. 2024. XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17753–17763, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks (Roth et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2024.lrec-main.1544.pdf