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

• URL: http://arxiv.org/abs/2508.00397v1
• Date: Fri, 01 Aug 2025 07:51:35 GMT
• Title: Video Forgery Detection with Optical Flow Residuals and Spatial-Temporal Consistency
• Authors: Xi Xue, Kunio Suzuki, Nabarun Goswami, Takuya Shintate,
• Abstract summary: We propose a detection framework that leverages spatial-temporal consistency by combining RGB appearance features with optical flow residuals.<n>By integrating these complementary features, the proposed method effectively detects a wide range of forged videos.
• Score: 1.7061868168035932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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