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

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.

Key Contributions

• Dual-branch architecture combining RGB appearance features with optical flow residuals to jointly model spatial and temporal anomalies in AI-generated videos
• Use of flow residuals (differences between consecutive flow maps) to amplify localized motion inconsistencies while suppressing global motion artefacts
• Evaluation across ten diffusion-based generative models on both text-to-video and image-to-video tasks, demonstrating state-of-the-art generalisation

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