Explainable Forensics of Manipulated Segments in Untrimmed Long Videos
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917556129890304 |
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| author | Feng, Yue Li, Jingjing Lu, Qijia Ji, Wei Zhang, Jingrou Shen, Fei Li, Xiao Jia, Yizhen Chen, Qiang Wang, Limin Li, Wentong Qin, Jie |
| author_facet | Feng, Yue Li, Jingjing Lu, Qijia Ji, Wei Zhang, Jingrou Shen, Fei Li, Xiao Jia, Yizhen Chen, Qiang Wang, Limin Li, Wentong Qin, Jie |
| contents | The rapid advancement of AI-driven video generation has transformed content creation, while simultaneously increasing the risk of misinformation through localized manipulations in long-form videos. Existing video forensic methods predominantly operate on short, independent clips, and thus fail to capture realistic scenarios where AI-generated content is sparsely embedded within otherwise authentic footage. To bridge this gap, we formulate the task of Temporal AI-Generated Segment Localization and Explanation, which targets authenticity detection, temporal localization, and interpretable analysis of manipulated segments in untrimmed long videos. We further introduce TASLE, a large-scale benchmark comprising 12,472 untrimmed videos with diverse manipulation patterns and rich annotation signals, including temporal boundaries, authenticity labels, and segment-level rationales. In addition, we propose MSLoc, a coarse-to-fine forensic baseline that combines a boundary-sensitive proposal generation module for efficient long-video scanning with an MLLM-based refinement module for precise boundary localization and interpretable reasoning. Experiments validate the effectiveness of the proposed baseline, highlighting the importance of segment-level explainable forensics for long-form AI-generated video analysis. Our dataset and code are publicly available at https://debby-0527.github.io/TASLE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_02402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Explainable Forensics of Manipulated Segments in Untrimmed Long Videos Feng, Yue Li, Jingjing Lu, Qijia Ji, Wei Zhang, Jingrou Shen, Fei Li, Xiao Jia, Yizhen Chen, Qiang Wang, Limin Li, Wentong Qin, Jie Computer Vision and Pattern Recognition The rapid advancement of AI-driven video generation has transformed content creation, while simultaneously increasing the risk of misinformation through localized manipulations in long-form videos. Existing video forensic methods predominantly operate on short, independent clips, and thus fail to capture realistic scenarios where AI-generated content is sparsely embedded within otherwise authentic footage. To bridge this gap, we formulate the task of Temporal AI-Generated Segment Localization and Explanation, which targets authenticity detection, temporal localization, and interpretable analysis of manipulated segments in untrimmed long videos. We further introduce TASLE, a large-scale benchmark comprising 12,472 untrimmed videos with diverse manipulation patterns and rich annotation signals, including temporal boundaries, authenticity labels, and segment-level rationales. In addition, we propose MSLoc, a coarse-to-fine forensic baseline that combines a boundary-sensitive proposal generation module for efficient long-video scanning with an MLLM-based refinement module for precise boundary localization and interpretable reasoning. Experiments validate the effectiveness of the proposed baseline, highlighting the importance of segment-level explainable forensics for long-form AI-generated video analysis. Our dataset and code are publicly available at https://debby-0527.github.io/TASLE. |
| title | Explainable Forensics of Manipulated Segments in Untrimmed Long Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.02402 |