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Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions

CORDIS · observation · Publication date unknown

the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions As challenges posed by misinformation become apparent in the modern digital society, state-of-the-art methods of Artificial Intelligence, especially Natural Language Processing (NLP) and Machine Learning, are considered as countermeasures. Indeed, previous research has shown that NLP solutions can detect phenomena such as fake news, social media bots or usage of propaganda techniques. However, little attention has been given to the robustness of these approaches, which is especially important in the case of deliberate misinformation, whose authors would likely attempt to deceive any automatic filtering algorithm to achieve their goals. The goal of the ERINIA project is to explore the robustness of text classifiers in this application area by investigating methods for detecting adversarial examples. Such methods aim to perform small perturbations to a given text piece, so that its meaning is preserved, but the output of the investigated classifier is reversed. To that end, previously unexplored dir

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recordType

award

status

CLOSED

region

EU

value

165312.96

unit

EUR