LBRT:用于图像复制移动伪造检测的局部信息精制变压器
Peng Liang1, Ziyuan Li1, Hang Tu1
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510630, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了本地分支改进变压器 (LBRT) 用于复制移动伪造检测 (CMFD). 通过完善本地细节和全球背景,LBRT增强了深度学习,优于现有的CMFD方法.
科学领域:
- 计算机视觉 计算机视觉
- 数字图像法医学 数字图像法医学
- 人工智能的人工智能
背景情况:
- 目前用于复制移动伪造检测 (CMFD) 的深度学习方法经常丢失关键细节信息.
- 现有的方法难以提取远程依赖和局部改区域细节.
研究的目的:
- 开发一个先进的深度学习模型用于复制移动伪造检测.
- 解决现有方法在捕捉全球背景和地方细节方面的局限性.
主要方法:
- 设计了一种新的双分支网络,即本地分支精炼变压器 (LBRT).
- 在全球和本地重新分割的图像补丁上,LBRT采用了变压器编码.
- 全球和本地分支机构的特征被合并,然后进行升级采样和解码.
主要成果:
- LBRT有效地建模了全球语义信息,并完善了本地细节信息.
- 实验结果表明,与最先进的CMFD方法相比,LBRT的性能优越.
- 该模型在USCISI,CASIA CMFD和DEFACTO CMFD数据集上实现了高精度.
结论:
- 拟议的LBRT网络在复制移动伪造检测方面取得了重大进展.
- 整合全球和本地特征改进可以提高CMFD系统的稳定性和准确性.
- LBRT为未来的图像法医研究提供了一个有希望的方向.
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