真实世界中多域内人脸伪造检测的合理性检验
A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World
针对多域人脸伪造检测中域差异主导特征空间的问题，提出模型无关的两阶段框架 DevDet，通过 Face Forgery Developer 放大真伪差异并结合 Dose-Adaptive 微调策略，使检测器在域未指定条件下仍能做出单帧独立判断，为真实场景下的深度伪造检测提供了可复用的训练范式。
原文依据 we propose a two-stage, model-agnostic framework termed DevDet
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论现有的深度伪造检测方法旨在开发具有泛化能力的检测器。尽管“泛化”可能是一劳永逸的终极目标，但在训练伪造样本和域有限的情况下，期望泛化能够覆盖完全未见过的变化似乎过于理想化，尤其是考虑到真实世界深度伪造的多样性、先进性和庞大体量。因此，引入大规模多域数据进行训练对于实际应用而言是可行且重要的。然而，在这种多域场景下，多个域之间的差异而非细微的真伪区分主导了特征空间。结果，尽管检测器能够在每个域内相对区分真伪（即高AUC），但在域未指定的条件下进行单图像真伪判断时却表现不佳（即低ACC）。本文首先定义了一种新的研究范式，称为多域内人脸伪造检测（MID-FFD），该范式包含足够数量的真伪域用于训练。然后，检测器应对域未指定的输入提供明确的真伪判断，这模拟了真实世界中逐帧独立检测的场景。同时，为了解决域主导问题，我们提出了一种两阶段的、模型无关的框架，称为DevDet（检测器开发者），以放大真伪差异并使其在特征空间中占据主导。DevDet由人脸伪造开发者（FFDev）和剂量自适应检测器微调策略（DAFT）组成。实验表明，我们在MID-FFD场景下有效预测真伪的优越性，同时保持了对未见数据的原始泛化能力。
英文原文摘要 1754 字符
Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" could be the ultimate target once and for all, with limited training forgeries and domains, it appears idealistic to expect generalization that covers entirely unseen variations, especially given the diversity, advancement, and vast volume of real-world deepfakes. Therefore, introducing large-scale multi-domain data for training can be feasible and important for real-world applications.However, within such a multi-domain scenario, the differences between multiple domains, rather than the subtle real/fake distinctions, dominate the feature space. As a result, despite detectors being able to relatively separate real and fake within each domain (i.e., high AUC), they struggle with single-image real/fake judgments in domain-unspecified conditions (i.e., low ACC).In this paper, we first define a new research paradigm named Multi-In-Domain Face Forgery Detection (MID-FFD), which includes sufficient volumes of real-fake domains for training. Then, the detector should provide definitive real-fake judgments to the domain-unspecified inputs, which simulate the frame-by-frame independent detection scenario in the real world. Meanwhile, to address the domain-dominant issue, we propose a two-stage, model-agnostic framework termed DevDet (\underline Dev eloper for \underline Det ector) to amplify real/fake differences and make them dominant in the feature space. DevDet consists of a Face Forgery Developer (FFDev) and a Dose-Adaptive detector Fine-Tuning strategy (DAFT). Experiments demonstrate our superiority in effectively predicting real-fake under the MID-FFD scenario while maintaining original generalization ability to unseen data.