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TSLEPS:用于保护传感器捕获图像的两级定位和删除方法

Xiaoxu Li^1,2, Jun Fu³, Jinjian Wang^1,2

¹Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alar 843300, China.

Sensors (Basel, Switzerland)

|August 28, 2025

概括

这项研究介绍了传感器捕获图像 (TSLEPS) 隐私保护的双阶段定位和删除方法. TSLEPS有效地检测和删除图像中的私人文本, 保持移动设备的视觉质量.

科学领域:

计算机视觉
图像处理
网络安全

背景情况:

越来越多的移动成像传感器引起了人们对图像隐私泄露的担忧.
目前的手动隐私保护方法效率低下, 降低图像质量.

研究的目的:

开发一种自动化和有效的方法来保护传感器捕获的图像中的敏感文本信息.
在隐私保护后保持图像完整性和可用性.

主要方法:

拟议的TSLEPS (传感器捕获图像中的隐私保护的双阶段定位和删除方法) 具有双阶段框架.
使用倒置的剩余注意力机制和通用的高效聚合层来检测目标.
采用了纹理增强特征注意力机制和对抗性生成网络来删除文本.
集成的半实例规范化用于移动设备的计算效率.

主要成果:

在隐私目标检测中实现了97.5%的准确性和96.4%的回忆.
达到了38.2140 dB的PSNR和0.9607的SSIM隐私文本删除质量.
在公开的真实世界隐私数据集上表现出有效的性能.

结论:

在传感器拍摄的图像中提供有效的隐私保护解决方案.
该方法显著提高了检测准确性,删除质量和计算效率.
TSLEPS适用于资源有限的移动设备.

关键词:

图像传感器对象检测个人隐私保护隐私文本删除视觉隐私

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