超越真实与虚假：面向意图的视频分析
Beyond Real versus Fake Towards Intent-Aware Video Analysis
AI总结：
本文提出IntentHQ基准，通过多模态模型分析视频意图，涵盖23种细粒度类别，用于识别视频背后的动机和目标。
摘要：
生成模型的快速发展导致了越来越逼真的深度伪造视频，给社会和安全带来了重大风险。尽管现有检测方法专注于区分真实与伪造视频，但这些方法未能回答一个根本问题：操纵视频的意图是什么？为此，我们引入了IntentHQ：一个用于以人类为中心的意图分析的新基准，将范式从真实性验证转向对视频的上下文理解。IntentHQ包含5168个视频，这些视频经过精心收集并标注了23种细粒度意图类别，包括“金融欺诈”、“间接营销”、“政治宣传”以及“制造恐惧”。我们使用监督和自监督的多模态模型进行意图识别，这些模型整合了时空视频特征、音频处理和文本分析，以推断视频背后的动机和目标。我们提出的模型被简化以区分各种意图类别。
英文摘要：
The rapid advancement of generative models has led to increasingly realistic deepfake videos, posing significant societal and security risks. While existing detection methods focus on distinguishing real from fake videos, such approaches fail to address a fundamental question: What is the intent behind a manipulated video? Towards addressing this question, we introduce IntentHQ: a new benchmark for human-centered intent analysis, shifting the paradigm from authenticity verification to contextual understanding of videos. IntentHQ consists of 5168 videos that have been meticulously collected and annotated with 23 fine-grained intent-categories, including "Financial fraud", "Indirect marketing", "Political propaganda", as well as "Fear mongering". We perform intent recognition with supervised and self-supervised multi-modality models that integrate spatio-temporal video features, audio processing, and text analysis to infer underlying motivations and goals behind videos. Our proposed model is streamlined to differentiate between a wide range of intent-categories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
