当生成式重放遇上不断演变的深度伪造：用于增量式人脸伪造检测的双混淆感知正则化
When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection
AI总结：
研究人脸生成技术发展下的增量式深度伪造检测问题，提出双混淆感知正则化策略双CARE，通过引入域感知混淆分数量化域混淆，对重放生成器和检测器进行双调制，有效利用生成式重放改进检测效果。
摘要：
人脸生成技术的快速发展带来了越来越多的伪造方法，使得增量式深度伪造检测对于维持强大的检测器至关重要。虽然生成式重放为缓解灾难性遗忘且无需存储历史数据提供了一个有前景的解决方案，但其有效性受到生成样本与真实数据之间的“域混淆”阻碍。我们观察到重放样本分为两类：当重放生成器与新引入的伪造模型非常相似时，生成的真实样本与伪造域重叠并变得“域有风险”；当生成器差异很大时，生成的样本保持更清晰的域分离，可被视为“域安全”。为应对这一挑战，我们提出了一种双混淆感知正则化策略，称为双CARE。引入了一个域感知混淆分数（DC分数）来量化域混淆，并对重放生成器和增量检测器的优化进行“双调制”。在DC分数的指导下，更新重放生成器以更好地逼近先前任务的分布，而检测器采用不同的监督策略：对域安全样本直接监督，对域有风险的样本使用相对分离损失进行调节以平衡监督和混淆。大量实验表明，双CARE有效地利用了生成式重放，并在不断演变的伪造场景下改进了增量式深度伪造检测。
英文摘要：
The rapid advancement of face generation techniques has introduced an increasing variety of forgery methods, making incremental deepfake detection essential for maintaining robust detectors. While generative replay provides a promising solution to alleviate catastrophic forgetting without storing historical data, its effectiveness is hindered by \textbf{domain confusion} between generated samples and real data. We observe that replay samples fall into two categories: when the replay generator closely resembles the newly introduced forgery model, generated real samples overlap with the fake domain and become \textbf{domain-risky}; when the generator differs significantly, generated samples maintain clearer domain separation and can be treated as \textbf{domain-safe}. To address this challenge, we propose a Dual \textbf{C}onfusion-\textbf{A}ware \textbf{RE}gularization strategy, termed \textbf{Dual-CARE}. A Domain-aware Confusion Score (DC Score) is introduced to quantify domain confusion and \textbf{dual-modulate} the optimization of both replay generators and the incremental detector. Guided by DC Score, replay generators are updated to better approximate previous-task distributions, while the detector adopts different supervision strategies: domain-safe samples are directly supervised, whereas domain-risky samples are regulated using a Relative Separation Loss to balance supervision and confusion. Extensive experiments demonstrate that Dual-CARE effectively exploits generative replay and improves incremental deepfake detection under evolving forgery scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
