用频率空间域进行双解,用于图像操纵定位
IEEE transactions on neural networks and learning systems
|October 15, 2024
概括
本研究介绍了一种用于图像操纵本地化 (IML) 的新型解表示学习网络 (DRN). 该DRN有效地分离了基本的痕迹特征,提高了检测准确性和在识别操纵图像中的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 数字法医学数字法医学
- 机器学习 机器学习
背景情况:
- 图像操纵本地化 (IML) 依赖于嵌入空间中的富含痕迹的特征.
- 现有的IML方法在操纵的痕迹特征中与冗余信息作斗争.
- 这种复杂性阻碍了对痕迹特征的充分理解,从而无法准确地定位.
研究的目的:
- 引入一种新的解表示学习网络 (DRN),以改进图像操纵本地化.
- 有效地将多领域信息解为与IML目标相关的表示.
- 为了提高图像操纵检测的准确性和稳定性.
主要方法:
- 为IML开发了一个脱代表学习网络 (DRN).
- 引入频率解模块 (FDM) 来分离低频和高频组件,减少冗余.
- 实现了一个空间脱模块 (SDM),使用通道激活地图来区分真实和操纵的表示.
主要成果:
- 拟议的DRN方法在三个公共基准 (CASIA,NIST,覆盖率) 中表现出卓越的表现.
- 与现有的最先进的IML方法相比,该网络实现了更强大的稳定性.
- 分离的高频组件作为有效的微量补充,改善特征聚合.
结论:
- 通过分离复杂的多域特征,DRN有效地解决了IML中冗余信息的挑战.
- 拟议的FDM和SDM模块显著提高了图像操纵本地化的精度和可靠性.
- 对于识别被操纵的图像,DRN提供了一个强大而高性能的解决方案.
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