一项关于面部呈现攻击检测机制的调查:迄今为止和未来的前景
Deepika Sharma1, Arvind Selwal1
1Department of Computer Science and Information Technology, Central University of Jammu, Samba, 181143 India.
概括
本综述探讨了面对面的反伪造技术,强调了诸如未知攻击和在演示攻击检测 (PAD) 中深度学习模型的有限数据集等挑战. 未来的研究应该解决这些问题,以获得更强大的面部识别系统.
科学领域:
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 面部识别 (FR) 系统正在推进安全认证.
- 安全威胁,特别是伪造攻击,在FR系统中越来越令人担忧.
- 呈现攻击检测 (PAD) 对于区分真实面孔和文物至关重要.
研究的目的:
- 系统地审查最先进的面部防伪技术.
- 提供关于面部PAD机制,文物,数据集和协议的见解.
- 确定面对PAD的未来研究方向.
主要方法:
- 对面部PAD的计算方法的审查,包括基于硬件的,手工制作的功能和深度学习方法.
- 对面部工件,性能协议 (HTER,ACA) 和基准数据集的分析概述.
- 在REPLAY-ATTACK数据集上分析了最近的十二种最先进的PAD技术.
主要成果:
- 目前面临的PAD机制显示出潜力,但面临着关键问题.
- 关键的挑战包括对未知攻击的有限概括以及对深度学习的不充分数据集.
- 手工制作的功能显示有限的歧视,需要使用较小数据集进行高效的深度学习PAD.
结论:
- 尽管取得了进展,但面对PAD需要进一步研究以解决泛化,数据限制和效率的问题.
- 由于COVID-19的流行,面部识别和PAD方法面临额外的挑战.
- 本综述旨在作为参考,并指导面部防伪的未来研究.
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