Abstract
The emergence and popularity of facial deepfake methods spur the vigorous development of deepfake datasets and facial forgery detection, which to some extent alleviates the security concerns about facial-related artificial intelligence technologies. However, when it comes to human body forgery, there has been a persistent lack of datasets and detection methods, due to the later inception and complexity of human body generation methods. To mitigate this issue, we introduce TikTok-DeepFake (TT-DF), a novel large-scale diffusion-based dataset containing 6,120 forged videos with 1,378,857 synthetic frames, specifically tailored for body forgery detection. TT-DF offers a wide variety of forgery methods, involving multiple advanced human image animation models utilized for manipulation, two generative configurations based on the disentanglement of identity and pose information, as well as different compressed versions. The aim is to simulate any potential unseen forged data in the wild as comprehensively as possible, and we also furnish a benchmark on TT-DF. Additionally, we propose an adapted body forgery detection model, Temporal Optical Flow Network (TOF-Net), which exploits the spatiotemporal inconsistencies and optical flow distribution differences between natural data and forged data. Our experiments demonstrate that TOF-Net achieves favorable performance on TT-DF, outperforming current state-of-the-art extendable facial forgery detection models. For our TT-DF dataset, please refer to this https URL.
Abstract (translated)
面部深度伪造技术的出现和流行推动了深度伪造数据集的发展以及面部伪造检测方法的进步，这在一定程度上缓解了与面部相关的人工智能技术的安全担忧。然而，在涉及人体伪造的情况下，由于生成方法起步较晚且复杂性高，一直缺乏相应的数据集和检测方法。为解决这一问题，我们引入了TikTok-DeepFake（TT-DF），这是一个基于扩散的新型大规模数据集，包含6,120个伪造视频和1,378,857个合成帧，专门用于人体伪造检测。TT-DF提供了多种伪造方法，包括利用多个先进的图像动画模型进行操作、基于身份与姿态信息解耦的不同生成配置以及不同压缩版本。我们的目标是尽可能全面地模拟野外可能出现的任何未见过的伪造数据，并且我们在TT-DF上提供了一个基准测试。此外，我们还提出了一种适应性的人体伪造检测模型——时序光流网络（TOF-Net），该模型利用自然数据和伪造数据之间的时空不一致性和光流分布差异来进行检测。实验结果表明，TOF-Net在TT-DF上的表现优于当前最先进的可扩展面部伪造检测模型。 有关我们TT-DF数据集的更多信息，请参阅此[链接](https://this-is-the-url-for-tt-df-data-set)（请将方括号中的内容替换为实际URL）。
URL
https://arxiv.org/abs/2505.08437
