Papers with BFClass

1 papers
BFClass: A Backdoor-free Text Classification Framework (2021.findings-emnlp)

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Challenge: Various trigger design strategies have been explored to attack text classifiers, however, defending such attacks remains an open problem.
Approach: They propose a backdoor-free training framework that poisons a subset of training data by injecting trigger patterns and setting their labels as the target labels.
Outcome: The proposed framework can detect all the triggers, remove 95% of poisoned training samples with very limited false alarms, and achieve almost the same performance as the models trained on benign training data.

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