Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification

Understand

It has been proved that deep neural networks are facing a new threat called backdoor attacks, where the adversary can inject backdoors into the neural network model through poisoning the training dataset.

• When the input containing some special pattern called the backdoor trigger, the model with backdoor will carry out malicious task such as misclassification specified by adversaries.
• In text classification systems, backdoors inserted in the models can cause spam or malicious speech to escape detection.
• Previous work mainly focused on the defense of backdoor attacks in computer vision, little attention has been paid to defense method for RNN backdoor attacks regarding text classification.