# Explainable Forensics of Manipulated Segments in Untrimmed Long Videos

arXiv: https://arxiv.org/abs/2606.02402 · HTML: https://mlsanity.com/paper/2606.02402/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 59 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 30 of 35)

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- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
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- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models](https://mlsanity.com/paper/2405.04233/) (2024)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark](https://mlsanity.com/paper/2405.19707/) (2024)
- [TRACE: Temporal Grounding Video LLM via Causal Event Modeling](https://mlsanity.com/paper/2410.05643/) (2024)
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- [AI-Generated Video Detection via Perceptual Straightening](https://mlsanity.com/paper/2507.00583/) (2025)
- [D3: Training-Free AI-Generated Video Detection Using Second-Order Features](https://mlsanity.com/paper/2508.00701/) (2025)
- [BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation](https://mlsanity.com/paper/2505.12620/) (2025)
- [BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM](https://mlsanity.com/paper/2507.14632/) (2025)
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- [RareAct: A video dataset of unusual interactions](https://mlsanity.com/paper/2008.01018/) (2020)
- [Spider: A Unified Framework for Context-dependent Concept Segmentation](https://mlsanity.com/paper/2405.01002/) (2024)
- [Generative AI for Film Creation: A Survey of Recent Advances](https://mlsanity.com/paper/2504.08296/) (2025)
- [MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps](https://mlsanity.com/paper/2503.18223/) (2025)
- [VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning](https://mlsanity.com/paper/2510.14672/) (2025)
- [MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection](https://mlsanity.com/paper/2510.21449/) (2025)
- [MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learning](https://mlsanity.com/paper/2507.12062/) (2025)
- [DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning](https://mlsanity.com/paper/2506.14827/) (2025)
- [SAM3-I: Segment Anything with Instructions](https://mlsanity.com/paper/2512.04585/) (2025)
- [MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations](https://mlsanity.com/paper/2503.13858/) (2025)

## Abstract (arXiv)

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.