An Efficient Character-Level Adversarial Attack Inspired by Textual Variations in Online Social Media Platforms

作     者：Jebran Khan Kashif Ahmad Kyung-Ah Sohn 

作者机构：Department of Artificial IntelligenceAjou UniversitySuwonKorea Department of Computer ScienceMunster Technological UniversityCorkIreland Department of Software and Computer EngineeringAjou UniversitySuwonKorea 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第12期

页      面：2869-2894页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Research Foundation of Korea (NRF)grant funded by the Korea government (MSIT) (No.NRF-2022R1A2C1007434) by the BK21 FOUR Program of the NRF of Korea funded by the Ministry of Education (NRF5199991014091) 

主　　题：Adversarial attack text classification social media character-level attack phonetic similarity visual similarity word importance rank beam search 

摘      要：In recent years,the growing popularity of social media platforms has led to several interesting natural language processing(NLP)***,these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning(ML)and NLP *** work presents a new low-level adversarial attack recipe inspired by textual variations in online social media *** 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 *** intuition of the proposed scheme is to generate adversarial examples influenced by human cognition in text generation on social media platforms while preserving human robustness in text understanding with the fewest possible *** intentional textual variations introduced by users in online communication motivate us to replicate such trends in attacking text to see the effects of such widely used textual variations on the deep learning *** this work,the four most commonly used textual variations are chosen to generate adversarial ***,this article introduced a word importance ranking-based beam search algorithm as a searching method for the best possible perturbation *** effectiveness of the proposed adversarial attacks has been demonstrated on four benchmark datasets in an extensive experimental setup.