Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning

2024-06-18

arXiv.org

AI Summary

This work addresses Privacy-sensitive Object Identification (POI), proposing a novel paradigm that transcends visual appearance by leveraging scene context for privacy attribute discrimination. Methodologically, it is the first to formulate POI as a visual reasoning task, constructing heterogeneous scene graphs to explicitly encode structural scene relationships. To improve generalization, a context perturbation oversampling strategy is introduced. Furthermore, a hybrid graph reasoning mechanism is proposed, jointly modeling node–node and edge–edge homophilous paths to enable fine-grained, context-aware inference. Evaluated on a multi-scene POI benchmark, the approach significantly outperforms state-of-the-art methods—particularly in distinguishing visually similar yet privacy-antonymous objects (e.g., medical syringes vs. toy syringes). Results demonstrate that explicit scene context modeling is critical for robust privacy semantics understanding, validating the efficacy of structured reasoning over raw visual features.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Knowledge Representation and Reasoning: Preferences

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The Privacy-sensitive Object Identification (POI) task allocates bounding boxes for privacy-sensitive objects in a scene. The key to POI is settling an object's privacy class (privacy-sensitive or non-privacy-sensitive). In contrast to conventional object classes which are determined by the visual appearance of an object, one object's privacy class is derived from the scene contexts and is subject to various implicit factors beyond its visual appearance. That is, visually similar objects may be totally opposite in their privacy classes. To explicitly derive the objects' privacy class from the scene contexts, in this paper, we interpret the POI task as a visual reasoning task aimed at the privacy of each object in the scene. Following this interpretation, we propose the PrivacyGuard framework for POI. PrivacyGuard contains three stages. i) Structuring: an unstructured image is first converted into a structured, heterogeneous scene graph that embeds rich scene contexts. ii) Data Augmentation: a contextual perturbation oversampling strategy is proposed to create slightly perturbed privacy-sensitive objects in a scene graph, thereby balancing the skewed distribution of privacy classes. iii) Hybrid Graph Generation&Reasoning: the balanced, heterogeneous scene graph is then transformed into a hybrid graph by endowing it with extra"node-node"and"edge-edge"homogeneous paths. These homogeneous paths allow direct message passing between nodes or edges, thereby accelerating reasoning and facilitating the capturing of subtle context changes. Based on this hybrid graph... **For the full abstract, see the original paper.**

Problem

• Identifying privacy-sensitive objects using scene context beyond visual appearances
• Resolving skewed class distribution through contextual perturbation oversampling
• Developing hybrid graph reasoning to capture subtle contextual relationships

Innovation

• Converts unstructured images into structured heterogeneous scene graphs
• Uses contextual perturbation oversampling to balance privacy class distribution
• Transforms scene graphs into hybrid graphs with homogeneous paths