Jebran Khan1, Kashif Ahmad2, Kyung-Ah Sohn1,3,*
Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159
Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language
processing (NLP) applications. However, these social media-based NLP applications are subject to different types
of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents
a new low-level adversarial attack recipe inspired by textual variations in online social media communication.
These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic
similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to
generate adversarial examples… More >
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