Papers by Noseong Park
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks (2021.acl-long)
Copied to clipboard
| Challenge: | Existing adversarial examples can fool ML models by generating a fixed phrase that can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class. |
| Approach: | They propose a honeypot-based defense framework that greedily searches and injects multiple trapdoors into an NN model to “bait and catch” potential attacks. |
| Outcome: | The proposed algorithm detects attacks with 99% TPR and less than 2% FPR while maintaining prediction accuracy within 1% margin. |
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to defend textual neural network models against adversarial attacks often require retraining and retrain . e.g., BERT, RoBERTa require great time and computation resources. |
| Approach: | They propose an algorithm that modifies and re-trains only the last layer of a textual NN and transforms it into a stochastic weighted ensemble of multi-expert prediction heads. |
| Outcome: | The proposed algorithm outperforms existing models against black-box attacks by 15%–70% . the proposed algorithm is based on a novel algorithm from software engineering . |
Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation? (2026.acl-long)
Copied to clipboard
| Challenge: | In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. |
| Approach: | They propose a low-rank adaptation approach that injects parameterized singular components with spectral clipping into the pre-trained model. |
| Outcome: | The proposed method improves performance and retains pre-trained knowledge while preserving the weights of the model. |