Rickard Stureborg


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Learning the Legibility of Visual Text Perturbations
Dev Seth | Rickard Stureborg | Danish Pruthi | Bhuwan Dhingra
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Many adversarial attacks in NLP perturb text in puts to produce visually similar strings (‘ergo’, ‘εrgo’) which are legible to humans but degrade model performance. Although preserving legibility is a necessary condition for text perturbation, little work has been done to systematically characterize it; instead, legibility is typically loosely enforced via intuitions around the nature and extent of perturbations. Particularly, it is unclear to what extent can inputs be perturbed while preserving legibility, or how to quantify the legibility of a perturbed string. In this work, we address this gap by learning models that predict the legibility of a perturbed string, and rank candidate perturbations based on their legibility. To do so, we collect and release LEGIT, a human-annotated dataset comprising the legibility of visually perturbed text. Using this dataset, we build both text- and vision-based models which achieve up to 0.91 F score in predicting whether an input is legible, and an accuracy of 0.86 in predicting which of two given perturbations is more legible. Additionally, we discover that legible perturbations from the LEGIT dataset are more effective at lowering the performance of NLP models than best-known attack strategies, suggesting that current models may be vulnerable to a broad range of perturbations beyond what is captured by existing visual attacks.

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Exploring the Effect of Frequency Resolution in FNet
Gregory Szumel | Ghazal Khalighinejad | Rickard Stureborg | Sam Wiseman
Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)