一个强大的合成面部探测器在OSN上下文基于色调梯度的功能
Tanusree Ghosh1, Ruchira Naskar1
1Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, India.
Forensic science international
|January 17, 2026
概括
本研究介绍了一种强大的方法来检测在线社交网络 (OSN) 上的合成图像. 该技术使用色通道和残余CNN中的渐变特征,即使在后处理图像中也有效.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 生成性AI的进步使得广泛的合成图像创建成为可能.
- 在线社交网络 (OSN) 面临着假冒内容传播的越来越多的挑战.
- 现有的合成图像探测器在现实世界OSN数据上经常失败.
研究的目的:
- 开发一种强大的合成图像探测器,能够抵御OSN扰动.
- 创建一个适合边缘设备的计算效率高的解决方案.
主要方法:
- 从颜色通道 (色度,光度) 中利用渐变特征.
- 使用低参数的,基于残余的卷积神经网络 (CNN).
- 评估各种数据集的性能,包括后处理和压缩图像.
主要成果:
- 在测试数据集上实现了合成图像检测的100%准确性.
- 与当代生成模型相比,表现出强大且一致的检测性能.
- 在模仿OSN循环 (后处理/压缩) 的图像上保持高精度和稳定性.
结论:
- 基于梯度的颜色通道功能与低复杂度的残余网络相结合,提供了有效的OSN弹性合成图像检测解决方案.
- 提出的方法是资源高效的,适合边缘部署.
- 解决了在线社交环境中可靠地检测假内容的关键需求.
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