Papers with adversarial defense methods
Synonym-unaware Fast Adversarial Training against Textual Adversarial Attacks (2025.findings-naacl)
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| Challenge: | Existing adversarial defense methods rely on predetermined linguistic knowledge and assume that attackers’ synonym candidates are known, which is often unrealistic. |
| Approach: | They propose a Fast Adversarial Training method that leverages single-step perturbation generation and effective perturbation initialization to improve model robustness without requiring synonym awareness. |
| Outcome: | Experiments show that the proposed method outperforms existing models under character-level and word-level attacks while still maintaining the correct syntax. |
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising (2025.acl-long)
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| Challenge: | Existing adversarial defense methods for natural language processing still pose challenges to adversarials. |
| Approach: | They propose a novel adversarial defense method that incorporates a diffusion layer as a denoiser between the encoder and the classifier. |
| Outcome: | The proposed method improves over existing adversarial defense methods and achieves state-of-the-art performance against black-box and white-box adversarials. |
ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)
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| Challenge: | Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness. |
| Approach: | They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks. |
| Outcome: | The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning. |