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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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以深度学习为驱动的多层级稳定图方法,以增强数据安全性.

Yousef Sanjalawe1, Salam Al-E'mari2, Salam Fraihat3

  • 1Department of Information Technology, King Abdullah II School for Information Technology, University of Jordan (JU), Amman, 11942, Jordan.

Scientific reports
|February 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种结合Huffman编码,LSB嵌入和深度学习的新型稳定图形框架,用于安全隐藏数据. 该方法提高了数字通信中的不可察觉性,稳定性和安全性.

关键词:
数据安全数据安全哈夫曼编码是什么意思图像嵌入式 图像嵌入式在LSB中嵌入LSB嵌入.史蒂冈图 (Steganography) 是一种隐藏的图形.

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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
  • 数字法医学数字法医学

背景情况:

  • 确保数据完整性,真实性和机密性在数字时代至关重要,因为连接性和安全威胁日益增加.
  • 传统的石学方法面临的局限性包括低有效载荷能力,可检测性和易受攻击的脆弱性.

研究的目的:

  • 通过提出一个新的多层次框架来解决传统隐形图的局限性.
  • 为了提高数据隐藏技术的不可察觉性,稳定性和安全性.

主要方法:

  • 整合哈夫曼编码用于数据压缩和统计模糊.
  • 最小显著位 (LSB) 嵌入,以有效地将数据插入封面图像中.
  • 一个基于深度学习的编码解码器模型,用于增强安全性和不可察觉性.

主要成果:

  • 结构相似度指标 (SSIM) 显示的高视觉保真率高于99%.
  • 在标准条件下实现了100%的文本恢复精度,表明了强大的数据检索.
  • 与传统方法相比,显著提高了对噪音和压缩等常见攻击的抵抗力.

结论:

  • 拟议的多层框架为数据隐藏提供了卓越的稳定性,安全性和计算效率.
  • 这种创新方法通过有效应对现代数据隐藏挑战,促进了安全的通信和数字权利管理.
  • 压缩,自适应嵌入和深度学习的结合提供了一个平衡的解决方案,用于隐形图的不可感知性和弹性.