IDGuard: Robust, General, Identity-Centric POI Proactive Defense Against Face Editing Abuse

In this work we propose IDGuard a novel proactive defense method from the perspective of developers to protect Persons-of-Interest (POI) such as national leaders from face editing abuse. We build a bridge between identities and model behavior safeguarding POI identities rather than merely certain face images. Given a face editing model IDGuard enables it to reject editing any image containing POI identities while retaining its editing functionality for regular use. Specifically we insert an ID Normalization Layer into the original face editing model and introduce an ID Extractor to extract the identities of input images. To differentiate the editing behavior between POI and nonPOI we use a transformer-based ID Encoder to encode extracted POI identities as parameters of the ID Normalization Layer. Our method supports the simultaneous protection of multiple POI and allows for the addition of new POI in the inference stage without the need for retraining. Extensive experiments show that our method achieves 100% protection accuracy on POI images even if they are neither included in the training set nor subject to any preprocessing. Notably our method exhibits excellent robustness against image and model attacks and maintains 100% protection performance when generalized to various face editing models further demonstrating its practicality.