标题： 基于光流残差和时空一致性的视频伪造检测 显示英文标题

标题： Video Forgery Detection with Optical Flow Residuals and Spatial-Temporal Consistency

摘要： 基于扩散的视频生成模型的快速发展导致了越来越逼真的合成内容，给视频伪造检测带来了新的挑战。 现有方法常常难以捕捉细粒度的时间不一致性，尤其是在具有高视觉保真度和连贯运动的AI生成视频中。 在本工作中，我们提出了一种检测框架，通过结合RGB外观特征与光流残差来利用时空一致性。 该模型采用双分支结构，其中一支分析RGB帧以检测外观级别的伪影，另一支处理流残差以揭示由不完美的时间合成引起的细微运动异常。 通过整合这些互补特征，所提出的方法有效地检测了各种伪造视频。 在十个不同生成模型上的文本到视频和图像到视频任务的大量实验表明了该方法的鲁棒性和强大的泛化能力。 显示英文摘要

摘要： The rapid advancement of diffusion-based video generation models has led to increasingly realistic synthetic content, presenting new challenges for video forgery detection. Existing methods often struggle to capture fine-grained temporal inconsistencies, particularly in AI-generated videos with high visual fidelity and coherent motion. In this work, we propose a detection framework that leverages spatial-temporal consistency by combining RGB appearance features with optical flow residuals. The model adopts a dual-branch architecture, where one branch analyzes RGB frames to detect appearance-level artifacts, while the other processes flow residuals to reveal subtle motion anomalies caused by imperfect temporal synthesis. By integrating these complementary features, the proposed method effectively detects a wide range of forged videos. Extensive experiments on text-to-video and image-to-video tasks across ten diverse generative models demonstrate the robustness and strong generalization ability of the proposed approach.