通过识别身份的多分支网络检测反法医深度假冒
1Dundee International Institute, Central South University, Changsha, China.
Frontiers in big data
|December 26, 2025
概括
本研究介绍了一种新的多道深度假冒检测框架,用于打击对抗性攻击. 该方法增强了对复杂的逃避技术的稳定性,同时保持了标准深度假冒检测的高精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数字法医学数字法医学
背景情况:
- 深度假冒检测系统擅长识别传统假冒,但与旨在逃避检测的对抗性样本作斗争.
- 敌对样本使用微妙的扰动来掩盖伪造文物,导致传统的二进制分类器错误地将它们归类为真实的.
- 现有的方法容易受到针对特定目标和欺骗检测算法的反法医技术的攻击.
研究的目的:
- 开发一个强大的深度假冒检测框架,能够识别传统和反法医 (对抗性) 深度假冒.
- 提高深度假冒检测系统对故意逃避策略的弹性.
- 为了提高真实,伪造和反法医媒体的分类准确性.
主要方法:
- 提出了用于深度假冒检测的多道特征提取框架.
- 采用三类分类策略来区分真实,伪造和反法医样本.
- 利用身份保护面部表征,空间和频率域特征进行全面分析.
主要成果:
- 拟议的方法显示了对抗法医攻击的强度的显著改进.
- 在检测常规深度假冒时保持高准确度.
- 在实验数据集中有效区分真实,伪造和对抗性深度假冒样本.
结论:
- 多道特征提取和三类分类方法为对抗深度假冒提供了增强的稳定性.
- 这一框架在复杂的逃避技术的存在下,为深度假冒检测提供了更可靠的解决方案.
- 这些发现有助于推进数字取证和媒体真实性验证领域.
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