Abstract
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.
Abstract (translated)
基于扩散模型的视频生成技术的迅速发展，使得合成内容越来越逼真，这给视频伪造检测带来了新的挑战。现有的方法往往难以捕捉细微的时间不一致性问题，尤其是在视觉上高度逼真且运动连贯的人工智能生成视频中。为此，在这项工作中我们提出了一种利用时空一致性的检测框架，该框架结合了RGB外观特征和光流残差。模型采用双分支架构：一个分支分析RGB帧以检测外观层面的瑕疵，另一个分支处理光流残差以揭示由于时间合成不完美导致的细微运动异常。通过整合这些互补特征，所提出的方法能够有效检测各种伪造视频。 在十个不同的生成模型上进行的文字到视频和图像到视频任务的广泛实验表明了该方法具有强大的鲁棒性和泛化能力。
URL
https://arxiv.org/abs/2508.00397
