图像隐形图像技术抵御统计隐形分析攻击:一个系统的文献综述
Richard Apau1, Michael Asante1, Frimpong Twum1
1Department of Computer Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana.
PloS one
|September 16, 2024
概括
本综述探讨了对统计石位分析有抗性的图像石位图技术. 生成对抗网络和人工智能驱动的方法正在成为主导,增强信息安全.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 网络安全 网络安全
背景情况:
- 隐藏在图像中的信息,或图像隐藏图像,越来越重要.
- 统计级分析技术正在出现,用于检测隐藏的消息,使图像易受网络攻击的影响.
- 现有的关于强大的图像隐形图像技术的研究是有限的.
研究的目的:
- 提供一个全面的系统文献审查的图像隐形图谱方案,耐受统计隐形分析的攻击.
- 更新研究人员和从业人员关于信息安全保证机制的当前趋势.
- 解决关于高级图像隐形图像的文献上的差距.
主要方法:
- 来自主要数字图书馆 (ACM,IEEE,Science Direct,Wiley) 的125篇文章的系统文献审查.
- 综合和分析使用PRISMA方法与定量和定性方法.
- 讨论技术对统计级分析 (例如RS,Chi-Square) 的稳定性.
主要成果:
- 生成对抗网络 (GAN) 正在主导当前的图像隐形图像研究.
- 机器学习,深度学习和卷积神经网络等人工智能 (AI) 算法提高了安全性.
- 传统的方法,如最小显著位 (LSB) 算法,正受到越来越少的关注.
结论:
- 人工智能和GAN代表了安全图像隐形图像技术的最前沿.
- 未来的研究应该探索像区块链和生物识别等新兴技术,以加强隐形图谱.
- 该研究强调了信息隐藏的不断变化的格局及其安全影响.
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