针对跨领域的反伪造的无重复和有效的持续学习
IEEE transactions on pattern analysis and machine intelligence
|August 20, 2025
概括
这项研究引入了一种新的方法,以防止面部防伪 (FAS) 模型在持续学习过程中忘记过去的攻击. 该方法通过专注于纹理信息和注意力一致性来增强对新伪造方法的概括性.
科学领域:
- 计算机视觉
- 人工智能
- 机器学习
背景情况:
- 面对反伪造 (FAS) 模型面对不断发展的伪造技术,需要强大的持续学习策略.
- 在FAS中减轻灾难性遗忘 (CF) 的现有方法对于微妙的伪造线索是不够的,并且缺乏泛化增强.
- 在FAS中的灾难性遗忘与视觉转换器 (ViT) 模型中的不一致的注意力模式有关.
研究的目的:
- 开发有效的FAS持续学习策略,减轻灾难性遗忘 (CF) 并改善对未见的伪造攻击的概括.
- 解决当前CF策略在处理伪造信息的不可察觉性方面的局限性.
- 提出一种提取一般化伪造纹理信息并保持注意力的一致性的方法.
主要方法:
- 为视觉变压器 (ViT) 微调提出了一个实例智能动态中心差异卷积适配器模块.
- 通过学习和重复使用ViT注意力矩阵来缓解灾难性遗忘 (CF) 的注意力一致性规范化.
- 开发了新的协议并进行了广泛的验证实验.
主要成果:
- 在面部防伪 (FAS) 持续学习过程中,该方法在减轻灾难性遗忘 (CF) 方面表现出卓越的表现.
- 这种方法显著提高了FAS模型对未见的伪造领域和攻击类型的概括能力.
- 这种方法有效地提取了通用的伪造纹理信息,并在学习过程中保持了注意力的一致性.
结论:
- 开发的动态适配器和注意力一致性规范化在面部防伪 (FAS) 中有效应对灾难性遗忘 (CF).
- 拟议的战略为构建能够应对不断变化的威胁的强大且适应性强的飞行系统提供了有希望的解决方案.
- 这些发现突显了注意力机制和纹理信息在持续学习环境中的普遍伪造检测的重要性.
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