{"ID":2892522,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14807","arxiv_id":"2507.14807","title":"Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection","abstract":"Multi-face deepfake videos are becoming increasingly prevalent, often appearing in natural social settings that challenge existing detection methods. Most current approaches excel at single-face detection but struggle in multi-face scenarios, due to a lack of awareness of crucial contextual cues. In this work, we develop a novel approach that leverages human cognition to analyze and defend against multi-face deepfake videos. Through a series of human studies, we systematically examine how people detect deepfake faces in social settings. Our quantitative analysis reveals four key cues humans rely on: scene-motion coherence, inter-face appearance compatibility, interpersonal gaze alignment, and face-body consistency. Guided by these insights, we introduce \\textsf{HICOM}, a novel framework designed to detect every fake face in multi-face scenarios. Extensive experiments on benchmark datasets show that \\textsf{HICOM} improves average accuracy by 3.3\\% in in-dataset detection and 2.8\\% under real-world perturbations. Moreover, it outperforms existing methods by 5.8\\% on unseen datasets, demonstrating the generalization of human-inspired cues. \\textsf{HICOM} further enhances interpretability by incorporating an LLM to provide human-readable explanations, making detection results more transparent and convincing. Our work sheds light on involving human factors to enhance defense against deepfakes.","short_abstract":"Multi-face deepfake videos are becoming increasingly prevalent, often appearing in natural social settings that challenge existing detection methods. Most current approaches excel at single-face detection but struggle in multi-face scenarios, due to a lack of awareness of crucial contextual cues. In this work, we devel...","url_abs":"https://arxiv.org/abs/2507.14807","url_pdf":"https://arxiv.org/pdf/2507.14807v1","authors":"[\"Juan Hu\",\"Shaojing Fan\",\"Terence Sim\"]","published":"2025-07-20T03:53:52Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}