PrivEdit: A Zero-Shot Interactive Image Privacy Editing System

摘要

In this paper, we present PrivEdit, a zero-shot, interactive image privacy editing system specifically designed for automated sensitive information desensitization. As social networks and smart devices proliferate, the risk of unintended privacy leakage grows, driving demand for personalized, controllable protection tools. PrivEdit is powered by natural-language instructions and integrates a Recognize-Anything model for robust detection and classification of sensitive objects (e.g., faces, license plates, ID cards), followed by GroundingDINO and SAM for high-precision mask extraction. User intents are parsed and disambiguated via GPT-4o, enabling selective target confirmation and iterative refinement. Finally, our editing module performs localized edits-such as adjustable blurring, mosaicking, or replacement via generative editing. With support for multi-round feedback and real-time modification, PrivEdit seamlessly handles both pre-recorded images and live streams, making it ideal for social-media pre-publishing, privacy data desensitization in enterprise or healthcare contexts, and intelligent surveillance applications. By unifying detection, segmentation, intent parsing, and localized editing into one coherent interface, PrivEdit delivers an end-to-end solution for safeguarding visual data. Supplementary materials including the demo video and slides are available at: https://drive.google.com/file/d/13jFBmYgZgxYQLPIAqCeaQhcyzZTHpf7N/view?usp=sharing