Shaif Chowdhury, Moshiur Rahman Tonmoy · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.28524 · cited by 0 works (OpenAlex) · open-access PDF
ビデオ生成モデルは、画像やテキストプロンプトから現実的なビデオ映像を作成することができます。最近の進歩の一部は、著作権の保護されたビジュアルコンテンツの再生に関する懸念を引き起こしました。 しかし、生成されたビデオで著作権の漏れの検出はほとんど未知のままです。この論文では、著作権の漏れの検出のための新しいベンチマークデータセットを導入し、それぞれ5画像を含む50の著作権の対象コンセプトを含みます。各コンセプトでは、画像+プロンプトのビデオ生成モデルとプロンプトのみのビデオ生成モデルを使用して作成された5つのビデオを持っています。その後、我々は、著作権のための漏れ
Machine translation (Meta M2M100); the English original follows.
Video generation models can create realistic-looking video footage from images and text prompts. Some of the recent advances have raised concerns about the reproduction of copyrighted visual content. However, copyright leakage detection in generated videos remains largely unexplored. In this paper, we introduce a new benchmark data set for copyright leakage detection, comprising 50 copyrighted concepts, each with 5 images. For each concept, we have 5 videos created using the image+prompt video generation model and the prompt-only video generation model. Following that, we develop an AI toolset based on a contrastive retrieval framework that formulates copyright leakage detection as a video-to-image retrieval problem by matching aggregated video frame embeddings against a database of copyrighted image embeddings. Our method uses a single similarity threshold and retrieves the most similar copyrighted reference as supporting evidence. We conducted experiments showing strong retrieval and detection performance, achieving 99.6\% detection accuracy with only a single false negative. These results show the effectiveness of the retrieval-based method for scalable and explainable copyright leakage detection in AI-generated videos. Data can be accessed publicly from https://zenodo.org/records/21386117 and the source code for this project is publicly available at https://github.com/Shaif95/video-cp-Leakage.
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Topics: Computer science, Scalability, Leakage (economics), Artificial intelligence, Information retrieval, Benchmark (surveying)
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