---
格式版本: 2
标题: "VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics"
原文链接: "https://arxiv.org/abs/2608.11201"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
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发布时间证据: "**\\[v1\\]** Tue, 11 Aug 2026 17:58:10 UTC (10,462 KB)"
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发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T10:57:24+08:00"
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发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T10:56:49+08:00"
入库时间: "2026-08-12T02:57:24.947Z"
来源平台: "arXiv 学术论文搜索"
搜索渠道: "source_template"
搜索词: "https://arxiv.org/search/?query=Meta&searchtype=all"
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内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
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AI优质: "否"
AI打分: 10
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "该论文主题为AI生成视频取证，与超节点/AI Rack/机柜级AI基础设施完全无关，不符合项目关注范围。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T10:57:29+08:00"
AI主题相关性: 0
AI来源权威性: 10
AI新颖性: 0
AI技术细节: 0
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采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11201"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:VidForensics-M1: Meta-Detection Reinforcement Learning with Verifiable Temporal Grounding for AI-Generated Video Forensics

Authors:[Bowei Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+B), [Zheng Lu](https://arxiv.org/search/cs?searchtype=author&query=Lu,+Z), [Yuhan Bian](https://arxiv.org/search/cs?searchtype=author&query=Bian,+Y), [Xinchen Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+X), [Xingming Shui](https://arxiv.org/search/cs?searchtype=author&query=Shui,+X), [Yuesheng Huang](https://arxiv.org/search/cs?searchtype=author&query=Huang,+Y), [Xuhuan Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+X), [Zihao Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+Z), [Yifan Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+Y), [Jun Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+J), [Xiu Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+X)

[View PDF](https://arxiv.org/pdf/2608.11201) [HTML (experimental)](https://arxiv.org/html/2608.11201v1)

> Abstract:Recent advances in video generation models have significantly improved the realism of synthetic videos, blurring the boundary between generated and authentic content and raising concerns about misinformation. Existing MLLM-based detectors mainly rely on supervised fine-tuning or label-level reinforcement learning, where coarse supervision limits generalization to unseen scenarios and emerging video generators. To overcome these limitations, we are the first to introduce \\textbf{meta-detection} into AI-generated video detection, enabling reliable forgery detection by jointly optimizing predicted labels and supporting evidence within reinforcement learning. This paradigm requires reliable evidence signals and effective mechanisms to integrate them into label-level optimization. Textual rationales provide semantic descriptions of forgery artifacts, but their generation and verification depend on external models, making supervision vulnerable to hallucinations and semantic biases. In contrast, temporal grounding provides more objective and verifiable evidence, as manipulated intervals can be precisely controlled during forgery construction. Based on this insight, we propose an automated data construction pipeline that generates paired real-fake videos by replacing temporal segments with boundary-frame-conditioned video generation models. Furthermore, we introduce \\textbf{Evidence-Guided Reward Redistribution}, which performs evidence-aware credit assignment by redistributing rewards among label-correct responses according to evidence quality. This preserves reliable label supervision while encouraging detectors to acquire fine-grained and verifiable forgery localization capabilities. Extensive experiments demonstrate that \\textbf{VidForensics-M1} effectively leverages verifiable temporal evidence to achieve robust and generalizable AI-generated video detection.

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | [arXiv:2608.11201](https://arxiv.org/abs/2608.11201) \[cs.CV\] |
|  | (or [arXiv:2608.11201v1](https://arxiv.org/abs/2608.11201v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11201](https://doi.org/10.48550/arXiv.2608.11201) |

## Submission history

From: Bowei Liu \[[view email](https://arxiv.org/show-email/5145a92b/2608.11201)\]  
**\[v1\]** Tue, 11 Aug 2026 17:58:10 UTC (10,462 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11201) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
