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A Dual-level Detection Method for Video Copy Detection

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arxiv 2305.12361 v1 pith:FHRGQXH4 submitted 2023-05-21 cs.CV

Tianyi Wang , Feipeng Ma , Zhenhua Liu , Fengyun Rao This is my paper · ORCID
classification cs.CV
keywords detectionvideocopymethoddual-leveltechnologyareaavailable
verification ladder T0 review T1 audit T2 compute T3 formal

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With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms. Meta AI hold Video Similarity Challenge on CVPR 2023 to push the technology forward. In this paper, we share our winner solutions on both tracks to help progress in this area. For Descriptor Track, we propose a dual-level detection method with Video Editing Detection (VED) and Frame Scenes Detection (FSD) to tackle the core challenges on Video Copy Detection. Experimental results demonstrate the effectiveness and efficiency of our proposed method. Code is available at https://github.com/FeipengMa6/VSC22-Submission.

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Cited by 1 Pith paper

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  1. Counteracting temporal attacks in Video Copy Detection

    cs.CV 2025-01 conditional novelty 4.0 of 10

    A scene-change-based frame selection method for video copy detection resists temporal attacks and reduces compute and storage needs by over half while keeping detection accuracy nearly unchanged.

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