用测试时训练提升突发新闻假视频检测能力
T$^\text{3}$SVFND: Towards an Evolving Fake News Detector for Emergencies with Test-time Training on Short Video Platforms
- 测试时训练结合多模态掩码语言建模，动态适应新数据分布
- 在突发事件场景下检测准确率显著提升，优于传统方法
- 适合应急舆情监控、社交媒体内容审核等实时场景
现有假新闻视频检测方法因不同事件间数据分布差异而泛化性差，尤其在突发事件中性能急剧下降。本文提出T³SVFND框架，采用测试时训练（TTT）策略增强鲁棒性。设计基于掩码语言建模（MLM）的自监督辅助任务，通过融合音频与视频上下文信息预测被遮蔽的文本内容。在测试阶段，模型利用该辅助任务动态适应测试数据分布。在公开基准上的大量实验表明，该方法在突发事件新闻检测中表现优异，显著优于基线模型。
原文摘要 · Abstract (English)
The existing methods for fake news videos detection may not be generalized, because there is a distribution shift between short video news of different events, and the performance of such techniques greatly drops if news records are coming from emergencies. We propose a new fake news videos detection framework (T$^3$SVFND) using Test-Time Training (TTT) to alleviate this limitation, enhancing the robustness of fake news videos detection. Specifically, we design a self-supervised auxiliary task based on Mask Language Modeling (MLM) that masks a certain percentage of words in text and predicts these masked words by combining contextual information from different modalities (audio and video). In the test-time training phase, the model adapts to the distribution of test data through auxiliary tasks. Extensive experiments on the public benchmark demonstrate the effectiveness of the proposed model, especially for the detection of emergency news.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。