在加密域中的图像法医
Yongqiang Yu1,2, Yuliang Lu1,2, Longlong Li1,2
1College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
检测伪造的加密图像至关重要. 本研究介绍了加密域 (IFED) 中的图像取证和深度学习模型,即轻量级增强取证网络 (LEFN),以识别加密图像中的复制移动攻击.
科学领域:
- 数字法医学数字法医学
- 图像处理 图像处理
- 密码学 密码学 密码学 密码学
背景情况:
- 传统的法医工具与加密的多媒体斗争.
- 伪造者的加密对数字法医构成了重大挑战.
- 图像法医在加密域 (IFED) 是一个新兴和关键的研究领域.
研究的目的:
- 在加密域 (IFED) 中正式引入和定义图像取证.
- 为了应对使用位加密在加密图像中检测复制移动伪造的挑战.
- 为自动IFED提出一种基于深度学习的新解决方案.
主要方法:
- 制定了IFED的正式定义和评估指标.
- 使用经典的代加密技术来模拟伪造的加密图像.
- 基于深度学习的轻量级增强法医网络 (LEFN) 已被开发用于伪造检测.
主要成果:
- 拟议的LEFN模型在检测加密图像中的复制移动更改方面表现出有效性.
- 广泛的实验验证了LEFN方案的性能和稳定性.
- 该研究为IFED提供了一个基础框架.
结论:
- 开发的LEFN为加密领域的自动图像取证提供了可行的解决方案.
- 在打击复杂的数字伪造方面,IFED至关重要.
- 这项研究推动了加密多媒体数字取证领域的发展.
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