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
概括
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.
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.
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