[2603.03163] Conditioned Activation Transport for T2I Safety Steering

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03163 (cs) [Submitted on 3 Mar 2026] Title:Conditioned Activation Transport for T2I Safety Steering Authors:Maciej Chrabąszcz, Aleksander Szymczyk, Jan Dubiński, Tomasz Trzciński, Franziska Boenisch, Adam Dziedzic View a PDF of the paper titled Conditioned Activation Transport for T2I Safety Steering, by Maciej Chrab\k{a}szcz and 5 other authors View PDF HTML (experimental) Abstract:Despite their impressive capabilities, current Text-to-Image (T2I) models remain prone to generating unsafe and toxic content. While activation steering offers a promising inference-time intervention, we observe that linear activation steering frequently degrades image quality when applied to benign prompts. To address this trade-off, we first construct SafeSteerDataset, a contrastive dataset containing 2300 safe and unsafe prompt pairs with high cosine similarity. Leveraging this data, we propose Conditioned Activation Transport (CAT), a framework that employs a geometry-based conditioning mechanism and nonlinear transport maps. By conditioning transport maps to activate only within unsafe activation regions, we minimize interference with benign queries. We validate our approach on two state-of-the-art architectures: Z-Image and Infinity. Experiments demonstrate that CAT generalizes effectively across these backbones, significantly reducing Attack Success Rate while maintaining image fidelity compared to unsteered generation...