中圖分類號：TP181文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2025.09.007
引用格式：周瑞豐，欒瑞鵬，張超. 可解釋的深度網(wǎng)絡抗噪音干擾性逐層評估方法［J］.網(wǎng)絡安全與數(shù)據(jù)治理，2025，44（9）：46-50.

Explainable methods for layer-wise evaluating noise resistance of deep networks

Abstract： In complex application scenarios, deep neural networks are susceptible to noise data interference. How to objectively, effectively, and reliably evaluate the noise resistance of deep networks has become one of the key issues in the development of intelligent technology. However, the existing evaluation methods can only evaluate the noise resistance of the whole network, and have no explainable theoretical basis. To address the above problems, based on Minkowski difference based linear separability measure, a layer-wise noise resistance evaluation method for deep neural networks is proposed, achieving an explainable evaluation of the network′s noise resistance performance. This method can quantitatively analyze the data mapping behavior of each hidden layer of the deep network, explain the influence mechanism of noise interference on each hidden layer of the network, and then establish an explainable noise resistance evaluation method for the network′s hidden layers.

Key words : noise resistance evaluation; explainability; neural network