Transferable Visual Identity Protection against Unauthorized Reuse in Closed-Source Image Generation

Image-conditioned generation raises concerns about unauthorized use of visual identity. Adversarial image protection offers a lightweight defense against such misuse with small perturbations. However, existing defenses are typically developed on accessible generative pipelines, and their transfer across modern closed-source editors remains insufficiently characterized. To this end, we propose T-VIP, a transferable visual identity protection method for closed-source models that embeds a misleading identity signal into the protected image. Specifically, T-VIP constructs a transferable identity representation space from an ensemble of image encoders with lightweight projectors amplifying identity-discriminative cues. It then optimizes a push-and-pull misleading objective that drives the protected representation away from the source identity while keeping it within an identity-relevant manifold. Extensive experiments demonstrate that T-VIP substantially outperforms existing protection methods across diverse image generators without requiring white-box access. More importantly, it effectively transfers to closed-source systems, including GPT Image 2, Nano Banana 2, and Grok Imagine, highlighting its practicality for real-world visual identity protection.