Abstract
Face forgery detection encompasses multiple critical tasks, including identifying forged images and videos and localizing manipulated regions and temporal segments. Current approaches typically employ task-specific models with independent architectures, leading to computational redundancy and ignoring potential correlations across related tasks. We introduce OmniFD, a unified framework that jointly addresses four core face forgery detection tasks within a single model, i.e., image and video classification, spatial localization, and temporal localization. Our architecture consists of three principal components: (1) a shared Swin Transformer encoder that extracts unified 4D spatiotemporal representations from both images and video inputs, (2) a cross-task interaction module with learnable queries that dynamically captures inter-task dependencies through attention-based reasoning, and (3) lightweight decoding heads that transform refined representations into corresponding predictions for all FFD tasks. Extensive experiments demonstrate OmniFD's advantage over task-specific models. Its unified design leverages multi-task learning to capture generalized representations across tasks, especially enabling fine-grained knowledge transfer that facilitates other tasks. For example, video classification accuracy improves by 4.63% when image data are incorporated. Furthermore, by unifying images, videos and the four tasks within one framework, OmniFD achieves superior performance across diverse benchmarks with high efficiency and scalability, e.g., reducing 63% model parameters and 50% training time. It establishes a practical and generalizable solution for comprehensive face forgery detection in real-world applications. The source code is made available at this https URL.
Abstract (translated)
面部伪造检测涵盖了多个关键任务，包括识别篡改的图像和视频以及定位被操纵的空间区域和时间片段。目前的方法通常采用特定于每个任务的独立架构模型，导致计算冗余并忽略相关任务之间的潜在关联性。我们引入了OmniFD，这是一个统一框架，在单一模型中共同解决了四个核心面部伪造检测任务，即图像分类、视频分类、空间定位以及时间定位。 该架构由三个主要组件组成： 1. **共享的Swin Transformer编码器**：从图像和视频输入中提取统一的4D时空表示。 2. **跨任务交互模块**：带有可学习查询的模块通过基于注意力机制的推理动态地捕捉任务间的相互依赖关系。 3. **轻量级解码头**：将细化后的表示转换为所有FFD（Face Forgery Detection）任务对应的预测。 广泛实验表明，OmniFD相对于特定于任务的模型具有优势。其统一的设计利用多任务学习来捕获跨任务的一般化表征，并特别支持细粒度的知识转移以帮助其他任务。例如，在引入图像数据的情况下，视频分类准确率提高了4.63%。 此外，通过将图像、视频和四个任务整合在一个框架内，OmniFD在多个基准测试上实现了高效且可扩展的卓越性能，例如减少了63%的模型参数量以及50%的训练时间。它为现实应用中的综合面部伪造检测提供了实际可行且通用的解决方案。 该研究的源代码可在上述提供的链接中获取。
URL
https://arxiv.org/abs/2512.01128
