  With the widespread use of deep learning system in many applications, the
adversary has strong incentive to explore vulnerabilities of deep neural
networks and manipulate them. Backdoor attacks against deep neural networks
have been reported to be a new type of threat. In this attack, the adversary
will inject backdoors into the model and then cause the misbehavior of the
model through inputs including backdoor triggers. Existed research mainly
focuses on backdoor attacks in image classification based on CNN, little
attention has been paid to the backdoor attacks in RNN. In this paper, we
implement a backdoor attack in text classification based on LSTM by data
poisoning. When the backdoor is injected, the model will misclassify any text
samples that contains a specific trigger sentence into the target category
determined by the adversary. The existence of the backdoor trigger is stealthy
and the backdoor injected has little impact on the performance of the model. We
consider the backdoor attack in black-box setting where the adversary has no
knowledge of model structures or training algorithms except for small amount of
training data. We verify the attack through sentiment analysis on the dataset
of IMDB movie reviews. The experimental results indicate that our attack can
achieve around 95% success rate with 1% poisoning rate.
