arXiv cs.AI· Yifan Wang, Junyu Lu, Shun Zhang, Chaozhuo Li, Fanliang Bu, Rong Yin, Lingbin Bu, Changrong Min, Peng Chen·· 2 小时前AI 评分34
MechScam 基准与 ScamCourt 辩论框架：机制级网络诈骗视频检测研究
Beyond Risk Cues: A Mechanism-Level Benchmark and Verifiable Debate for Scam Video Detection on the Web
AI 导读
研究者推出 MechScam 基准与 ScamCourt 辩论框架，用于机制级诈骗视频检测。MechScam 包含 6,614 个视频，覆盖多样诈骗机制与非诈骗类别，提供视频证据支撑的解释、诈骗元素标注及基于真实证据构建的误导性论证。ScamCourt 通过控辩双方辩论加经核验引用的裁判决策，表现优于更大规模 MLLM 和同骨干微调基线，降低误报并更能抵抗误导性论证，数据集与代码已开源。
正文 · 原文
Abstract:Online scam videos pose significant threats to individuals and society. Existing studies primarily focus on binary classification, offering limited insights into how scams work and the reasoning behind detection decisions. Moreover, they often overlook the challenge of distinguishing scam videos from superficially similar non-scam content, such as advertisements, and the robustness of detection models against misleading arguments. These limitations hinder reliable scam video detection. To address these limitations, we introduce MechScam, a benchmark comprising 6,614 videos covering diverse scam mechanisms and non-scam categories. MechScam provides explanations supported by video evidence, annotations of potential scam elements and their context, and misleading arguments constructed from genuine evidence. Recognizing that genuine evidence can still be used to justify misleading conclusions, we further propose ScamCourt, a debate framework in which trained prosecution and defense debaters present opposing arguments, while a trained judge makes decisions based on a transcript with verified citations. Extensive experiments show that state-of-the-art MLLMs struggle to distinguish scam videos from similar non-scam content. ScamCourt outperforms larger MLLMs and same-backbone fine-tuned baselines, reducing false positives and showing stronger resistance to misleading arguments. Together, MechScam and ScamCourt contribute to more reliable and interpretable scam video detection. The dataset and code are available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.06110 [cs.AI] |
| (or arXiv:2611.06110v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06110 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yifan Wang [view email]
[v1]
Wed, 4 Nov 2026 07:52:43 UTC (4,141 KB)
来源：arXiv cs.AI · arxiv.org