基于生成对抗网络的医学诊断模型知识蒸馏对抗攻击方法

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基于生成对抗网络的医学诊断模型知识蒸馏对抗攻击方法

更新时间：2025-07-09

- 基于生成对抗网络的医学诊断模型知识蒸馏对抗攻击方法
- Issue 7, Pages: 76-85(2022)
- 作者机构：
  
  1. 西安邮电大学计算机学院
  2. 西安交通大学自动化科学与工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： R-05;TP391.41
- Published：2022
- 稿件说明：
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[J]. 2022, (7): 76-85.
DOI：

[J]. 2022, (7): 76-85. DOI：

摘要

为了提高医学诊断模型防御攻击的能力，提出了一种基于生成对抗网络的医学诊断模型知识蒸馏对抗攻击方法。首先创建医学对抗攻击端到端训练网络，并以残差网络作为对抗网络架构；其次在生成器特征块中融合扩张卷积块和通道注意力机制，采用马尔可夫判别器改进判别器网络结构；最后利用生成器和判别器组建生成对抗网络，使用对抗样本进行知识蒸馏对抗攻击，以训练医学诊断模型提高识别精度。采用对抗样本对所提对抗方法进行攻击验证，结果表明：本文方法对抗攻击的成功率为92.6%

与所对比的主流方法相比，该方法的成功率提高了20%

生成对抗样本的最大平均差异降低了3.68%

峰值信噪比、结构相似性分别提升了5.07%、20.29%。本文方法解决了医学诊断模型在对抗攻击中难以获取网络结构和参数信息的问题，生成的对抗样本更接近真实样本，网络效果更佳，为辅助医疗模型诊断及模型安全性提供了参考方案。

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