An Efficient Two-Stage Machine Unlearning Framework for Poisoned Specific Emitter Identification

specific emitter identification (SEI) is capable of identifying individuals from varying radiation sources, and it has been widely used in both military and civilian fields. Current deep-learning-based SEI methods typically demand substantial training samples. However, their performance deteriorates significantly when confronted with abnormal or poisoned samples, such as those subjected to impersonation attacks or label flipping attacks. In this article, we propose an efficient machine-unlearning-based algorithm with knowledge distillation and noise generation, to deal with the negative effects of forgettable poisoned samples in trained SEI deep network models while keeping the identification accuracy for the retaining sample set. Specifically, we first establish a feasible two teachers-one student-based knowledge distillation framework without any training restrictions, to achieve the coarse-grained student network for unlearning ability in poisoned SEI model. In addition, based on the constructed loss-maximizing noise generation model for forgettable samples, we design an available machine unlearning algorithm with impair-repair paradigm-based weight manipulations, to further remove the residual poisoned sample effects in the trained student network model and improve the SEI accuracy for the retaining samples. Finally, a series of experiments are implemented on our synthetic dataset and the public ORACLE dataset, and the results demonstrate that the proposed method can achieve the average accuracy of 98.34% on the retain set and 0.54% on the forgettable set. and the efficiency of our unlearning method is almost 10 times faster than that of other retrain-based methods.