用于深度假冒分类的单层KAN:在资源有限的环境中平衡效率和性能
Nadeem Jabbar1,2, Sohail Masood Bhatti1,2, Muhammad Rashid3
1Faculty of Computer Science and Information Technology, The Superior University, Lahore, Pakistan.
PloS one
|July 9, 2025
概括
本研究介绍了一种轻量级的Kolmogorov-Arnold网络 (KAN),用于在边缘设备上高效地检测深度假冒. 与传统方法相比,KAN在显著减少计算资源的情况下实现了高精度.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 网络安全 网络安全
背景情况:
- 深度假冒,人工智能生成的合成媒体,对数字内容的真实性构成重大威胁.
- 传统的深度假冒检测方法,如卷积神经网络 (CNN),是计算密集的,限制了它们在资源有限的设备上的使用.
- 对于边缘设备上高效的实时深度假冒检测的需求至关重要.
研究的目的:
- 评估单层科尔摩戈罗夫-阿诺德网络 (KAN) 对于深度假冒分类的有效性.
- 在准确性,内存足迹,参数数量和FLOP方面评估KAN的性能.
- 确定KAN是否适合在边缘设备上部署,以实时检测深度假冒.
主要方法:
- 一个单层的Kolmogorov-Arnold网络 (KAN) 拥有200个节点,用于深度假冒分类.
- 在基准数据集上评估了KAN模型:FaceForensics++和Celeb-DF.
- 包括精度,内存使用,参数计数和浮点运算 (FLOP) 在内的性能指标被测量并与最先进的CNN相比较.
主要成果:
- 在FaceForensics++数据集上,KAN实现了95.01%的准确性,在Celeb-DF数据集上达到88.32%的准确性.
- KAN模型表现出显著的效率,只需要52.4MB的内存,13.11万个参数和2621万个FLOP.
- 这些结果表明,与现有的基于CNN的方法相比,计算资源的大量减少.
结论:
- 科尔莫戈罗夫-阿诺德网络 (KAN) 为边缘设备上的深度假冒检测提供了一个可行的和高效的解决方案.
- KAN的低资源要求使其适合在智能手机和物联网系统上实时应用.
- 未来的研究应该探索KAN对抗对抗攻击的稳定性及其在数字媒体取证中的更广泛应用.
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