An automatic modulation identification confrontation defense method based on confrontation robustness evaluation
core set selection comprises the following steps: S1,
data set division and preprocessing: giving an original training
data set for preprocessing; s2, adversarial sample generation and robustness test: for each sample in the
training set, generating an adversarial sample set with different disturbance intensities by using a plurality of adversarial
attack methods; s3, sample robustness
score calculation: calculating a comprehensive robustness
score of each sample based on the adversarial
attack trajectory, and taking the comprehensive robustness
score as a core basis for
sample selection; s4, multi-level
core set selection based on robustness scoring: according to the ARS scores of the samples, combining
signal-to-
noise ratio distribution and category balance, adopting a hierarchical
selection strategy to construct a
core set: arranging the samples in the
training set in a descending order according to the ARS scores; dividing the sample into high, medium and low robustness intervals according to a preset layering threshold value; based on the target sampling rate k, adaptively determining the
sample selection number of each interval;
stratified sampling is further carried out in each interval according to a
signal-to-
noise ratio and a category, so that diversity is ensured; integrating the samples selected in each interval to form a final robust core set; and S5, adversarial defense model training and
verification: adversarial training is performed on the deep neural network by using the defense core set.