Papers by Dev Seth
Learning the Legibility of Visual Text Perturbations (2023.eacl-main)
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| Challenge: | Existing adversarial attacks in NLP perturb text to produce visually similar strings ('ergo', 'rgo') which are legible to humans but degrade model performance. |
| Approach: | They use a human-annotated dataset comprising the legibility of visually perturbed text to build models that predict the legible inputs and rank them based on their legibility. |
| Outcome: | The proposed models achieve an F score of 0.91 and an accuracy of 0.86 in predicting which of two perturbations is more legible. |