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HAG-NET:隐藏数据和使用生成对抗网络进行对抗性攻击.

Haiju Fan1, Jinsong Wang1

  • 1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了HAG-NET,这是一种使用对抗式稳定图示例 (ASEs) 的新型水印方法,以保护图像载体数据免受深度神经网络 (DNN) 攻击. HAG-NET有效地保护运营商数据,同时允许秘密数据恢复.

关键词:
敌对攻击是对抗性的攻击.深度学习是一种深度学习.生成性的对抗性网络.图像信息 (entropy) 是一个图像信息.在水印上使用水印.

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科学领域:

  • 计算机科学 计算机科学
  • 信息安全 信息安全
  • 人工智能的人工智能

背景情况:

  • 现有的水印技术难以保护载体数据免受复杂的阶段分析,特别是那些采用对抗性干扰的技术.
  • 深度神经网络 (DNN) 对微小的干扰非常敏感,这对隐形图谱方法中的载体数据完整性构成风险.

研究的目的:

  • 提出HAG-NET,这是一种新的水印方法,可以联合训练编码器,解码器和攻击者来保护载体数据.
  • 为了生成对抗性的石图示例 (ASE),这些示例对目标分类网络具有强大防护性,从而保护运营商形象.
  • 确保解码器能够准确地从这些受保护的ASEs中恢复秘密数据.

主要方法:

  • 该HAG-NET方法采用一个联合训练的编码器,解码器和攻击者框架.
  • 编码器生成了旨在与目标分类网络对抗的ASE,从而保护运营商数据.
  • 解码器被训练来恢复嵌入在生成的ASE中的秘密信息.

主要成果:

  • 在生成MNIST和CIFAR-10数据集上的ASEs时,HAG-NET的平均成功率超过了99%.
  • 由HAG-NET生成的ASEs表现出增强的稳定性,增加了约3.32%的攻击能力.
  • 图像信息度测量证实,HAG-NET与其他具有类似扰乱级别的生成式stego示例相比,嵌入了更多的信息.

结论:

  • HAG-NET提供了一种强大的解决方案,用于保护水印应用中的载波数据免受基于DNN的稳定分析.
  • 该方法成功地平衡了运营商数据保护与秘密信息的嵌入和恢复.
  • HAG-NET通过利用对抗性示例来加强数据保护,代表了安全隐形图的进步.