As deepfake videos become increasingly difficult for people to recognise, understanding the strategies humans use is key to designing effective media literacy interventions. We conducted a study with 195 participants between the ages of 21 and 40, who judged real and deepfake videos, rated their confidence, and reported the cues they relied on across visual, audio, and knowledge strategies. Participants were more accurate with real videos than with deepfakes and showed lower expected calibration error for real content. Through association rule mining, we identified cue combinations that shaped performance. Visual appearance, vocal, and intuition often co-occurred for successful identifications, which highlights the importance of multimodal approaches in human detection. Our findings show which cues help or hinder detection and suggest directions for designing media literacy tools that guide effective cue use. Building on these insights can help people improve their identification skills and become more resilient to deceptive digital media.

翻译：随着深度伪造视频日益难以被人眼识别，理解人类使用的检测策略对于设计有效的媒体素养干预措施至关重要。我们开展了一项包含195名21至40岁参与者的研究，参与者需判断真实与深度伪造视频，评估其判断置信度，并报告所依赖的视觉、听觉及知识层面的线索特征。实验结果表明，参与者对真实视频的识别准确率高于深度伪造视频，且对真实内容的预期校准误差更低。通过关联规则挖掘，我们识别出影响检测性能的线索组合模式。成功的检测往往同时涉及视觉外观、声音特征与直觉判断，这凸显了多模态方法在人类检测中的重要性。我们的研究揭示了有助于或阻碍检测的线索类型，并为设计引导有效线索使用的媒体素养工具提供了方向。基于这些发现，可以帮助人们提升识别能力，增强对欺骗性数字媒体的抵御力。