Deep learning has been widely adopted in natural language processing applications in recent years. Many existing studies show the vulnerabilities of machine learning and deep learning models against adversarial examples. However, most existing works currently focus on evasion attack on text data instead of positioning attack, also named backdoor attack. In this paper, we systematically study the backdoor attack against models on text data. First, we define the backdoor attack on text data. Then, we propose the different attack strategies to generate trigger on text data. Next, we propose different types of triggers based on modification scope, human recognition and special cases. Last, we evaluate the backdoor attack and the results show the excellent performance of with 100 %DIFDELCMD < \\%%% %DIF < backdoor attack rate and sacrificing of 0.71\\% on text classification text.abstract 
Deep learning has been widely adopted in natural language processing applications in recent years. Many existing studies show the vulnerabilities of machine learning and deep learning models against adversarial examples. However, most existing works currently focus on evasion attack on text data instead of positioning attack, also named backdoor attack. In this paper, we systematically study the backdoor attack against models on text data. First, we define the backdoor attack on text data. Then, we propose the different attack strategies to generate trigger on text data. Next, we propose different types of triggers based on modification scope, human recognition and special cases. Last, we evaluate the backdoor attack and the results show the excellent performance of with 100 %DIFDELCMD < \\%%% %DIF < backdoor attack rate and sacrificing of 0.71\\% on text classification text.abstract \% backdoor attack rate and sacrificing of 0.71\% on text classification text.
A
\% backdoor attack rate and sacrificing of 0.71\% on text classification text.