Papers by Jaesung Lee

2 papers
BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing Attacks (2025.findings-naacl)

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Challenge: Phishing often targets victims through visually perturbed texts to bypass security systems . previous studies have used synthetic datasets that do not contain real-world phishing cases .
Approach: They propose a visual perturbation dataset to target phishing attacks using visual perturbations.
Outcome: The proposed dataset includes real-world phishing cases annotated with visual perturbations . language models trained on the proposed dataset achieved an accuracy of approximately 96% .
Suppressing Final Layer Hidden State Jumps in Transformer Pretraining (2026.findings-eacl)

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Challenge: Existing models exhibit only slight changes in the angular distance between the input and output hidden state vectors in the middle layers .
Approach: They propose a jump-suppressing regularizer which penalizes large hidden state displacements near the final layer during pre-training.
Outcome: The proposed method significantly reduces hidden state jumps in the final layer and increases model capacity.

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