arXiv cs.AI· Yifan Wang, Junyu Lu, Shun Zhang, Chaozhuo Li, Fanliang Bu, Rong Yin, Lingbin Bu, Changrong Min, Peng Chen·· 12 小时前AI 评分31
MechScam 基准与 ScamCourt 辩论框架：面向网络诈骗视频检测的机制级评测与可验证辩论
Beyond Risk Cues: A Mechanism-Level Benchmark and Verifiable Debate for Scam Video Detection on the Web
AI 导读
研究者提出 MechScam 基准，包含 6,614 个视频，覆盖多种诈骗机制与非诈骗类别，并提供视频证据支撑的解释、潜在诈骗元素及上下文标注，以及由真实证据构造的误导性论证。
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