Beyond Real Versus Fake: Towards Intent-Aware Video Analysis

The rapid advancement of generative models has led to increasingly realistic deepfake videos, posing significant societal and security risks. While existing detection methods have focused 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: the first 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 spatiotemporal video features, audio processing, and text analysis to infer underlying motivation and goals behind videos. Our proposed model is streamlined to differentiate between a wide range of intent-categories. This work establishes intent recognition as a new research direction beyond authenticity verification, enabling richer contextual understanding of video. The IntentHQ dataset is available under https://github.com/atrey-a/intent-hq.