将深伪检测视为持续学习问题，提升模型对新伪造技术的适应能力。
Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization
- 构建基于真实时间演进的持续学习框架，模拟7年深伪技术发展过程。
- 实现比全量重训快155倍的高效适应，但对未来生成器泛化能力近似随机（FWT-AUC≈0.5）。
- 提出新假设：每种生成器有独特痕迹，导致现有方法难以泛化到未知伪造技术。
深伪生成技术的快速演进给检测系统带来严峻挑战，传统非持续学习方法需频繁且昂贵地重新训练。本文将深伪检测（DFD）重构为持续学习（CL）问题，提出一种高效框架，可增量式适应新兴视觉操纵技术，同时保留对过往生成器的知识。该框架不依赖虚构的训练序列，而是模拟过去7年真实世界中深伪技术的演化过程。同时，采用轻量级视觉骨干网络，支持深伪检测系统的实时运行。此外，我们提出两个新指标：历史性能度量连续AUC（C-AUC）与未来泛化度量前向转移AUC（FWT-AUC）。通过超过600次的实验验证，我们发现尽管能实现高效适应（比全量重训快155倍）并稳健保留历史知识，但当前方法在无额外训练的情况下对未知生成器的泛化能力接近随机（FWT-AUC ≈ 0.5），这归因于每个已有生成器的独特指纹特征。这一现象成为我们提出的‘非普适深伪分布假设’的基础。代码将在论文接受后公开。
原文摘要 · Abstract (English)
The rapid evolution of deepfake generation technologies poses critical challenges for detection systems, as non-continual learning methods demand frequent and expensive retraining. We reframe deepfake detection (DFD) as a Continual Learning (CL) problem, proposing an efficient framework that incrementally adapts to emerging visual manipulation techniques while retaining knowledge of past generators. Our framework, unlike prior approaches that rely on unreal simulation sequences, simulates the real-world chronological evolution of deepfake technologies in extended periods across 7 years. Simultaneously, our framework builds upon lightweight visual backbones to allow for the real-time performance of DFD systems. Additionally, we contribute two novel metrics: Continual AUC (C-AUC) for historical performance and Forward Transfer AUC (FWT-AUC) for future generalization. Through extensive experimentation (over 600 simulations), we empirically demonstrate that while efficient adaptation (+155 times faster than full retraining) and robust retention of historical knowledge is possible, the generalization of current approaches to future generators without additional training remains near-random (FWT-AUC $\approx$ 0.5) due to the unique imprint characterizing each existing generator. Such observations are the foundation of our newly proposed Non-Universal Deepfake Distribution Hypothesis. \textbf{Code will be released upon acceptance.}
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