[2603.04896] Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs

Computer Science > Artificial Intelligence arXiv:2603.04896 (cs) [Submitted on 5 Mar 2026] Title:Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs Authors:Lianyu Wang, Meng Wang, Huazhu Fu, Daoqiang Zhang View a PDF of the paper titled Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs, by Lianyu Wang and 3 other authors View PDF HTML (experimental) Abstract:The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP protection should proactively confine model deployment within authorized domains and prevent unauthorized transfers. However, existing methods rely on static training-time definitions, limiting flexibility in dynamic environments and often producing opaque responses to unauthorized inputs. To address these limitations, we propose a novel dynamic authorization with legality-aware intellectual property protection (AoD-IP) for VLMs, a framework that supports authorize-on-demand and legality-aware assessment. AoD-IP introduces a lightweight dynamic authorization module that enables flexible, user-controlled authorization, allowing users to actively specify or switch authorized domains on demand at deployment time. This enables the model to adapt seamlessly as application scenarios evolve and provides substantially greater extensibility than...