The embodiment of the invention provides a method for protecting the safety of a neural
network model, and the method comprises the steps: obtaining a neural
network model which comprises a plurality of network
layers obtained through training of training data; for any first
network layer, under the condition that parameters of other network
layers are fixed, performing first parameter adjustment on the first
network layer by using the training data to obtain a first fine adjustment model; determining a first index value of a preset
performance index corresponding to the first
fine tuning model, wherein the index value of the preset
performance index depends on the relative size of the corresponding model, the test loss on the
test data and the training loss on the training data; similarly, performing second parameter adjustment on the first
network layer by using the training data and the
test data to obtain a second fine adjustment model, and determining a second index value; and based on the relative size of the first index value and the second index value, determining the information sensitivity corresponding to the first network layer, and when the information sensitivity is greater than a predetermined threshold, performing security
processing on the first network layer.