Multimodal-Based Selective De-Identification Framework

Abstract

Selective de-identification is a key technology for protecting sensitive objects in visual data while preserving meaningful information. This study proposes a framework that leverages text prompt-based zeroshot and referring object detection techniques to accurately identify and selectively de-identify sensitive objects without relying on predefined classes. By utilizing state-of-the-art models such as GroundingDINO, objects are detected based on natural language prompts, and de-identification-via blurring or masking-is applied only to the corresponding regions, thereby minimizing information loss while achieving a high level of privacy protection. Experimental results demonstrate that the proposed method outperforms conventional batch de-identification approaches in terms of scalability and flexibility.