对深层面验证系统的全面调查对抗攻击和防御策略
Sohair Kilany1, Ahmed Mahfouz2,3
1Computer Science Department, Faculty of Science, Minia University, Al Minya, Egypt. sohair_kilany@mu.edu.eg.
Scientific reports
|August 22, 2025
概括
面部验证系统很容易受到攻击, 这项调查回顾了深度面部验证的进展,对抗性示例生成,以及增强安全的防御策略.
科学领域:
- 计算机科学
- 人工智能
- 生物识别
背景情况:
- 面部验证 (FV) 系统被广泛用于身份验证,但容易受到敌对攻击.
- 深度神经网络为光伏系统提供动力可以被人类无法察觉的敌对例子所欺骗.
- 在FV系统中的漏洞引起了安全关键应用的担忧.
研究的目的:
- 提供深层面验证近期进展的全面概述.
- 检查针对光伏系统生成对抗示例的算法.
- 在面部验证中审查防御机制.
主要方法:
- 对深度面部验证算法,数据库利用,协议和应用的文献审查.
- 深入分析最先进的对抗性示例生成技术.
- 检查当前的防御策略,以抵御对象对FV系统的攻击.
主要成果:
- 深度光伏系统表现出了显著的性能,但很容易受到对手的攻击.
- 在测试和部署过程中,敌对的例子可能会损害光伏系统的完整性.
- 有广泛的算法设计,数据库策略和协议存在于FV.
结论:
- 随着FV的广泛应用,需要对抗敌对攻击的强有力的防御.
- 了解对抗性示例生成对于制定有效的缓解策略至关重要.
- 这项调查为安全深层面验证的研究提供了基础资源.
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