针对多层次检测数字视频伪造的先进框架
Upasana Singh1, Sandeep Rathor1, Manoj Kumar2
1Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India.
Annals of the New York Academy of Sciences
|November 19, 2024
概括
这项研究引入了一个新的框架,用于检测复杂的多层视频伪造. 注意增强卷积神经网络 (AACNN) 框架在识别复杂的伪造视频内容方面实现了高精度.
科学领域:
- 计算机视觉 计算机视觉
- 数字法医学数字法医学
- 人工智能的人工智能
背景情况:
- 数字媒体的增长引发了人们对伪造视频传播和滥用的担忧.
- 目前的伪造检测方法与复杂,层次 (多层次) 的伪造作斗争.
研究的目的:
- 开发一个创新的框架来检测复杂的两级和三级视频伪造.
- 解决现有技术在识别复杂伪造内容方面的局限性.
主要方法:
- 利用注意力增强卷积神经网络 (AACNNs) 来从伪造的框架中提取复杂的特征.
- 采用基于U-Net的CycleGAN来准确地定位造区域.
- 集成的无模型的超学习,以提高检测的稳定性和准确性.
- 开发并使用一个定制的数据集,代表复杂的伪造场景.
主要成果:
- 在10次拍摄的场景中,AACNN框架实现了98.2%的准确性.
- 具有96.3%的灵敏度,97.6%的特异性和96.8%的F1分数,表现出高性能.
- 通过地方和全球关注机制,成功识别了两级和三级伪造品.
结论:
- 拟议的框架显著提高了复杂的视频伪造检测的准确性和可靠性.
- 整合AACNNs和CycleGAN为复杂的数字取证挑战提供了一个强大的解决方案.
- 这项研究为打击滥用先进伪造视频内容提供了一个关键工具.
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