使用"线索地图"进行面部反伪造
Liang Yu Gong1, Xue Jun Li1, Peter Han Joo Chong1
1Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了面部反伪造的新型深度学习方法,显著提高了检测呈现攻击的准确性. 该方法实现了最先进的性能,提高了面部识别系统的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 面部识别系统容易受到伪造攻击,这对在线金融安全构成风险.
- 由于数据采集要求,现有的多模式防伪方法可能昂贵.
- 迫切需要强大的反伪造解决方案,具有强大的泛化能力.
研究的目的:
- 提出一种新的表达式学习方法,用于面部反伪造.
- 开发一种具有成本效益的解决方案,克服多模式方法的局限性.
- 提高伪造攻击检测模型的概括能力.
主要方法:
- 一种使用基于Swin变压器和ResNet.Net的自动编码器结构的表示学习方法.
- 监督训练与交叉损失,半硬三重损失和光滑L1像素智能损失的组合.
- 一种构架,包括用于特征提取的编码器,用于生成"线索地图"的解码器和用于决策的辅助分类器.
主要成果:
- 拟议的模型在流行的假冒数据库 (CelebA,OULU,CASIA-MFSD) 上取得了卓越的性能.
- 数据集内部实验的平均分类错误率 (ACER) 低,分别为1.2%和1.6%.
- 跨数据集实验设置了一个新的最先进的性能,在重复攻击数据集上有一半总错误率 (HTER) 为23.8%.
结论:
- 开发的基于自动编码器的表示学习方法有效地检测了面部伪造攻击.
- 该方法表现出卓越的概括能力,并优于现有的反伪造模型.
- 这项研究在保护面部识别系统免受呈现攻击方面取得了重大进展.
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