源代码免费的域名适应与域名通用化培训面对反伪造
IEEE transactions on pattern analysis and machine intelligence
|February 27, 2024
概括
这项研究引入了一个新的框架,用于使用无源域调整 (SFDA) 保护隐私的面部防伪 (FAS). 它有效地减轻了域名和身份偏见,在不访问敏感源数据的情况下增强了概括性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于深度学习的面对反伪造 (FAS) 在不同数据集的概括性方面扎.
- 无源域调整 (SFDA) 提供了一种保护隐私的方法,但在减轻域和身份偏差方面面临挑战.
- 现有的SFDA方法在没有获得原始培训数据的情况下适应模型的能力是有限的.
研究的目的:
- 提出一个新的,完整的,保护隐私的框架,用于面对反伪造 (SDA-FAS) 的无源域调整.
- 在无源环境中系统地解决源模型预培训,知识适应和目标数据探索方面的挑战.
- 提高面部防伪模型的通用性,同时保持数据隐私和安全.
主要方法:
- 开发了一种使用因果关系启发的PatchMix数据增强来减少域偏差的通用源模型预训练方法.
- 设计了一个补丁明智的对比损失,以减轻身份偏见.
- 引入了对比域调整模块,用于跨域的条件分布调整和自我监督学习,并增加了补丁混合,用于探索目标数据和识别未见的攻击类型.
主要成果:
- 拟议的SDA-FAS框架有效地减轻了无源设置中的域名和身份偏见.
- 该框架通过自我监督的目标数据探索,在识别未见的攻击类型方面表现出卓越的性能.
- 在19个交叉数据集场景中进行了广泛的实验,显示出与最先进的方法相比有了显著的改进.
结论:
- SDA-FAS框架代表了第一个全面的,保护隐私的解决方案,用于解决面对反伪造的泛化问题.
- 建议的预培训,适应和探索方法对于在FAS中强大的SFDA至关重要.
- 该框架显著提高了面部防伪系统在现实应用中的稳定性和通用性.
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