内容适应的LSB隐形图与突出融合,ACO分散和混合加密与消去研究的混合加密
Ahmed Aljughaiman1, Rana Alrawashdeh2
1Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, 31982, Al-Ahsa, Saudi Arabia. aaaljughaiman@kfu.edu.sa.
Scientific reports
|December 30, 2025
概括
本研究介绍了一种新的图像隐形图像框架,使用内容适应性最小显著位 (LSB) 嵌入和殖民地优化 (ACO) 来提高安全性和隐形性. 该方法实现了高无感知度和强大的抵抗力,与现代的steganalysis技术相抗衡.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数字图像处理 数字图像处理
背景情况:
- 图像隐形摄影面临着平衡不可察觉性,嵌入能力和安全性的挑战.
- 现有的方法经常在视觉扭曲和统计检测能力方面扎.
- 需要先进的技术来安全地和隐形地将数据嵌入到数字图像中.
研究的目的:
- 提出一个适应内容的最小显著位 (LSB) 隐形图框架.
- 增强隐藏数据的不可察觉性,嵌入能力和保密性.
- 提高对基于统计和深度学习的稳定分析的抵抗力.
主要方法:
- 在低灵敏度图像区域使用突出导向嵌入,以最大限度地减少视觉扭曲.
- 实现了基于群优化 (ACO) 的嵌入索引的空间分散,以防止集群.
- 采用高级加密标准-加/计数模式 (AES-GCM) 和RSA-最佳不对称加密填充 (RSA-OAEP) 的集成混合加密.
- 使用RSA衍生种子进行可再生提取的嵌入指数的确定性再生.
主要成果:
- 实现了几乎无损的无感知度,峰值信号噪声比 (PSNR) 值在59.7-64.5dB之间,结构相似度指数 (SSIM) 值高于0.999.
- 在不同嵌入容量 (Bits Per Pixel - BPP) 上,表现出稳定的速率扭曲行为,对视觉质量的影响最小.
- 与基于卷积神经网络 (CNN) 的稳定分析器相比,表现出随机级别的检测能力,表明高安全性.
结论:
- 拟议的框架有效地平衡了图像隐形图像中的不可察觉性,容量和安全性.
- 内容适应的LSB嵌入与基于ACO的分散提供了卓越的隐形性和不可检测性.
- 混合加密和确定性索引再生确保保密性和可靠的数据提取.
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