Abstract
As modern text-to-image (T2I) models draw closer to synthesizing highly realistic content, the threat of unsafe content generation grows, and it becomes paramount to exercise control. Existing approaches steer these models by applying Euclidean adjustments to text embeddings, redirecting the generation away from unsafe concepts. In this work, we introduce hyperbolic control (HyCon): a novel control mechanism based on parallel transport that leverages semantically aligned hyperbolic representation space to yield more expressive and stable manipulation of concepts. HyCon reuses off-the-shelf generative models and a state-of-the-art hyperbolic text encoder, linked via a lightweight adapter. HyCon achieves state-of-the-art results across four safety benchmarks and four T2I backbones, showing that hyperbolic steering is a practical and flexible approach for more reliable T2I generation.
Maria Rosaria Briglia
PhD Student
Hello everyone! My name is Maria Rosaria, a Ph.D. student in AI Security, based in Sapienza University. My main research interest is in developing adversarial techniques in the generative AI domain, with a particular focus on Diffusion Model’s technology, and applying them also to the world of Explainable AI. My main research topics are Diffusion Models, Adversarial Machine Learning and Explainble AI by counterfactual examples.
Paolo Cursi
PhD Student
My principal research interests are robustness and unlearning methods for Flow Matching and Mechanistic Interpretability on generative models
