English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Natural Language Processing (NLP) models based on Machine Learning (ML) aresusceptible to adversarial attacks -- malicious algorithms that imperceptiblymodify input text to force models into making incorrect predictions. However,evaluations of these attacks ignore the property of imperceptibility or studyit under limited settings. This entails that adversarial perturbations wouldnot pass any human quality gate and do not represent real threats tohuman-checked NLP systems. To bypass this limitation and enable properassessment (and later, improvement) of NLP model robustness, we have surveyed378 human participants about the perceptibility of text adversarial examplesproduced by state-of-the-art methods. Our results underline that existing textattacks are impractical in real-world scenarios where humans are involved. Thiscontrasts with previous smaller-scale human studies, which reported overlyoptimistic conclusions regarding attack success. Through our work, we hope toposition human perceptibility as a first-class success criterion for textattacks, and provide guidance for research to build effective attack algorithmsand, in turn, design appropriate defence mechanisms.

How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks