基于RDS-YOLOv5功能增强转换的图像改检测
Meilong Zhu1, Mingda Li2, Zhaohui Wang2
1China Telecom Research Institute, Beijing, 102209, China. zhuml5@chinatelecom.cn.
Scientific reports
|October 31, 2024
概括
这项研究介绍了RDS-YOLOv5,这是一个增强的深度学习模型,用于检测被改的图像. 该方法提高了检测准确度和对图像操纵的稳定性,增强了数字安全性.
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:
- 恶意改图像对社会稳定和个人安全构成重大威胁.
- 由于数据集的特异性和特征提取,现有的改检测技术在一般化和效率方面面临限制.
- 需要强大而准确的方法来检测被改的图像.
研究的目的:
- 建议使用RDS-YOLOv5进行改进的改图像检测方法,并增强功能.
- 通过多通道功能增强融合算法来增强改痕迹的检测.
- 提高改图像识别模型的稳定性和性能.
主要方法:
- 开发了一个多通道功能增强融合算法来突出改文物.
- 一个改进的深度学习模型RDS-YOLOv5被设计用于改图像识别.
- 在SIOU损失函数中集成了面积比的非线性损失指标,以优化训练.
- RDS-YOLOv5是使用原始和增强图像特征的融合进行训练的.
主要成果:
- 与原始YOLOv5模型相比,RDS-YOLOv5表现出性能改善,F1-Score的增长率为6.46%,mAP50的增长率为5.13%,mAP95.15的增长率为3.15%.
- 集成SRIOU损失函数提高了模型定位改区域的能力2.54%.
- 使用合并数据集的培训进一步提高了总体检测性能约1%.
结论:
- 拟议的RDS-YOLOv5方法有效地提高了改图像检测能力.
- 功能增强和改进的损失功能有助于更强大,更准确的检测系统.
- 这项研究在数字取证学中为识别恶意图像操纵提供了有希望的进步.
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