Recent advances in AIGC have exacerbated the misuse of malicious deepfake content, making the development of reliable deepfake detection methods an essential means to address this challenge. Although existing deepfake detection models demonstrate outstanding performance in detection metrics, most methods only provide simple binary classification results, lacking interpretability. Recent studies have attempted to enhance the interpretability of classification results by providing spatial manipulation masks or temporal forgery segments. However, due to the limitations of forgery datasets, the practical effectiveness of these methods remains suboptimal. The primary reason lies in the fact that most existing deepfake datasets contain only binary labels, with limited variety in forgery scenarios, insufficient diversity in deepfake types, and relatively small data scales, making them inadequate for complex real-world scenarios.To address this predicament, we construct a novel large-scale deepfake detection and localization (\textbf{DDL}) dataset containing over $\textbf{1.4M+}$ forged samples and encompassing up to $\textbf{80}$ distinct deepfake methods. The DDL design incorporates four key innovations: (1) \textbf{Comprehensive Deepfake Methods} (covering 7 different generation architectures and a total of 80 methods), (2) \textbf{Varied Manipulation Modes} (incorporating 7 classic and 3 novel forgery modes), (3) \textbf{Diverse Forgery Scenarios and Modalities} (including 3 scenarios and 3 modalities), and (4) \textbf{Fine-grained Forgery Annotations} (providing 1.18M+ precise spatial masks and 0.23M+ precise temporal segments).Through these improvements, our DDL not only provides a more challenging benchmark for complex real-world forgeries but also offers crucial support for building next-generation deepfake detection, localization, and interpretability methods.

翻译：AIGC（人工智能生成内容）的近期进展加剧了恶意深度伪造内容的滥用，使得开发可靠的深度伪造检测方法成为应对这一挑战的关键手段。尽管现有的深度伪造检测模型在检测指标上表现出色，但大多数方法仅提供简单的二分类结果，缺乏可解释性。近期研究尝试通过提供空间篡改掩码或时间伪造片段来增强分类结果的可解释性。然而，由于伪造数据集的局限性，这些方法的实际效果仍不理想。主要原因在于，现有深度伪造数据集大多仅包含二值标签，伪造场景种类有限，深度伪造类型多样性不足，且数据规模相对较小，难以应对复杂的现实场景。为解决这一困境，我们构建了一个新颖的大规模深度伪造检测与定位（DDL）数据集，包含超过140万+伪造样本，涵盖多达80种不同的深度伪造方法。DDL的设计包含四项关键创新：（1）全面的深度伪造方法（覆盖7种不同的生成架构，总计80种方法），（2）多样化的篡改模式（整合7种经典和3种新颖的伪造模式），（3）多样化的伪造场景与模态（包括3种场景和3种模态），以及（4）细粒度的伪造标注（提供118万+精确空间掩码和23万+精确时间片段）。通过这些改进，我们的DDL不仅为复杂的现实伪造提供了更具挑战性的基准，还为构建下一代深度伪造检测、定位与可解释性方法提供了关键支持。