SCOPE: Bridging Explicit and Implicit Privacy Leakage for Quantitative Image Privacy Evaluation

The rapid growth of social media has led to the widespread uploading of private images to online networks, raising significant privacy concerns. Existing methods for image privacy assessment typically operate at coarse granularity, lack support for personalized settings, and primarily focus on explicit, entity-centered content. However, such approaches neglect implicit privacy, which refers to private information inferred from contextual cues rather than from any single identifiable visual entity, leading to a substantial underestimation of entire privacy risks. In this work, we propose SCOPE (Systematic Context-based Observation for Privacy Evaluation), a unified framework that systematically incorporates both explicit and implicit privacy across the entire image privacy lifecycle, including detection, quantitative risk assessment, and protection. SCOPE integrates context-aware image graphs with a concept-anchored ontology graph, enabling the incorporation of multi-source information to infer implicit privacy risks at both object and event levels. It further introduces novel qualitative and quantitative privacy metrics that jointly assess image-level privacy risks based on explicit and implicit content, and provides explainable mechanisms to guide implicit privacy protection. Experimental results demonstrate that SCOPE achieves 97.02% object-level and 88.37% event-level implicit privacy inference accuracy, outperforming previous methods by 15.88% and 21.83%, respectively. Extensive experiments and a user study further confirm the effectiveness of our privacy assessment metrics and protection mechanisms.