CrossDF:通过深度信息分解改进跨领域深度假冒检测
Shanmin Yang1, Hui Guo2, Shu Hu3
1Computer Science and Technology, Chengdu University of Information Technology, Chengdu, China.
Frontiers in big data
|December 5, 2025
概括
本研究引入了深度信息分解 (DID) 框架,以加强跨数据集深度假冒检测. DID框架改善了通过各种深度假冒技术识别操纵的媒体,提高了公众的信任和安全.
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:
- 深度假冒技术对公众安全和信任构成重大风险.
- 目前的深度假冒检测方法在交叉数据集概括方面扎,在遇到新型操纵技术时失败.
- 现有的方法往往专注于特定的视觉异常,限制了它们的稳定性.
研究的目的:
- 为跨数据集深度假冒检测 (CrossDF) 开发一个强大的框架.
- 提高深度假冒检测模型对隐形操纵技术的概括能力.
- 通过各种数据集和方法提高深度假冒识别的可靠性.
主要方法:
- 提出了一个深度信息分解 (DID) 框架,将面部表现分解为与深度假冒相关的和无关的组件.
- 专注于高层次的语义属性,而不是低层次的视觉文物进行分类.
- 引入了对抗性的相互信息最小化策略,以增强特征可分离性和折叠关系学习.
主要成果:
- 在从FF++到CDF2.2的交叉数据集评估中获得了0.779的AUC.
- 在基于扩散的文本到图像数据集上,最先进的AUC从0.669提高到0.802.
- 证明了DID框架对未见的深度假冒技术的卓越有效性和稳定性.
结论:
- 拟议的DID框架在跨数据集深度假冒检测方面取得了重大进展.
- 通过专注于语义属性和采用对抗性学习,该框架实现了更好的概括.
- DID框架增强了对新型操纵技术的稳定性,有助于更可靠的媒体身份验证.
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