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神经噪音打印传输:一种基于噪音打印的通用反法医框架
1Department of Electrical and Computer Engineering, Military Technical College, Cairo, Egypt.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一个神经噪声和印迹传输框架,以对抗数字图像的法医研究. 这种新的方法有效地将真实的噪音印记转移到伪造的图像中,大大降低了法医准确度,并创建了高保真输出.
科学领域:
- 数字法医学数字法医学
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 噪音印记,独特的摄像机文物,对于法医图像分析至关重要.
- 目前用于 noiseprint 提取的深度学习方法是强大的,但易受复杂的反法医攻击.
- 噪音印记和图像之间的复杂关系使得它们很难被操纵.
研究的目的:
- 提出一个新的神经噪声和打印传输框架,用于反法医.
- 开发可以使基于噪音印记的法医分析无效的方法.
- 为了创建视觉上不可察觉的图像操纵,欺骗法医工具.
主要方法:
- 提出了一个神经噪音印记传输框架,利用深度内容和噪音印记表示.
- 实施了两种方法:基于优化的方法和使用训练有素的神经注入器的基于 noiseprint 注入的方法.
- 这些方法是通用的,不需要对图像或摄像机模型进行特殊培训.
主要成果:
- 拟议的框架显著降低了伪造本地化和摄像头源识别任务中的法医准确度得分.
- 在DSO-1数据集中,法医准确度得分的平均降低达到75%.
- 生成的图像保持了高保真度,平均PSNR为31.5dB,SSIM为0.9.
结论:
- 神经噪音印记传输框架有效地对抗基于噪音印记的法医技术.
- 开发的方法成功地创建欺骗性的图像,同时保持视觉质量.
- 这些方法的通用性使其在数字图像法医和反法医领域具有广泛的适用性.
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