Deep learning has been widely adopted in natural language processing applicationsin recent years. Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   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 the 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 \% backdoor attack success rate and sacrificing of 0.71\% on text classification task.%DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > backdoor attacks success rate and sacrificing of 0.83\\% on the text classification task.
Recently, advanced NLP models have seen a surge in the usage of various applications . This raises the security threats of the released models. In addition to the clean models' unintentional weaknesses,{\em i.e., adversarial attacks, the poisoned models with malicious intentions are much more dangerous in real life. However, most existing works currently focus on the adversarial attacks on NLP models instead of positioning attacks , also named backdoor attacks . In this paper, we first propose thenatural backdoor attacks on NLP models. Moreover, we exploit the various attack strategies to generate trigger on text data and investigate different types of triggers based on modification scope, human recognition , and special cases. Last, we evaluate the backdoor attacks, and the results show the excellent performance of with 100 %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > backdoor attacks success rate and sacrificing of 0.83\\% on the text classification task.
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Deep learning has been widely adopted in natural language processing applicationsin recent years. Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples
Recently, advanced NLP models have seen a surge in the usage of various applications
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This raises the security threats of the released models. In addition to the clean models' unintentional weaknesses,
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i.e.,
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adversarial attacks, the poisoned models with malicious intentions are much more dangerous in real life.
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evasion attack on text data
the adversarial attacks on NLP models
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attack
attacks
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backdoor attack
backdoor attacks
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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
first propose the
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natural backdoor attacks
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on NLP models. Moreover, we exploit the various attack
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. Next, we propose
and investigate
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the
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,
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attack
attacks,
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\% backdoor attack success rate and sacrificing of 0.71\% on text classification task.