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相关实验视频

Updated: May 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

452

改进了基于CNN预测的可逆数据,隐藏图像.

Yingqiang Qiu1, Wanli Peng2, Xiaodan Lin1

  • 1College of Information Science & Engineering, Huaqiao University, Xiamen 361021, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
概括

这项研究引入了一种改进的卷积神经网络预测器 (ICNNP),用于隐藏在图像中的可逆数据. ICNNP通过预测像素复杂度来提高性能,以实现更高效的数据嵌入.

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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 密码学 密码学 密码学 密码学

背景情况:

  • 可逆数据隐藏 (RDH) 对于在图像中嵌入秘密信息而不会永久扭曲至关重要.
  • 使用卷积神经网络预测器 (CNNP) 的现有方法有希望,但可以改进以获得更好的速率扭曲性能.

研究的目的:

  • 提出一个改进的卷积神经网络预测器 (ICNNP),用于增强隐藏在图像中的可逆数据.
  • 与现有的基于CNNP的方案相比,实现更高的速率扭曲性能.

主要方法:

  • 拟议的ICNNP包括特征提取,像素预测和复杂性预测模块.
  • 图像被划分为子图像,ICNNP从另一个子图像中预测一个子图像.
  • 预测错误按复杂度进行排序,较不复杂的错误用于通过直方图移动嵌入数据.

主要成果:

  • ICNNP有效地预测了像素的复杂性,使数据嵌入更有效.
  • 拟议的方案显示了比标准的基于CNNP的方案更优越的速率扭曲性能.
  • 实验结果验证了ICNNP对RDH的有效性.

结论:

  • 开发的ICNNP提供了一种有效的方法,用于可逆数据隐藏,并提高性能.
  • 预测像素复杂性是实现低扭曲和高容量的数据嵌入的关键.
  • ICNNP代表了图像隐形图像技术的重大进步.
关键词:
在美国,CNN是CNN.组织谱图的转移 转移多任务处理是多任务处理.隐藏可逆数据 隐藏可逆数据

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