Hyperbolic Safety-Aware Vision-Language Models
Authors: name: Tobia Poppi, name: Tejaswi Kasarla, name: Pascal Mettes, name: Lorenzo Baraldi, name: Rita Cucchiara

Addressing the retrieval of unsafe content from vision-language models such
as CLIP is an important step towards real-world integration. Current efforts
have relied on unlearning techniques that try to erase the model's knowledge of
unsafe concepts. While effective in reducing unwanted outputs, unlearning
limits the model's capacity to discern between safe and unsafe content. In this
work, we introduce a novel approach that shifts from unlearning to an awareness
paradigm by leveraging the inherent hierarchical properties of the hyperbolic
space. We propose to encode safe and unsafe content as an entailment hierarchy,
where both are placed in different regions of hyperbolic space. Our HySAC,
Hyperbolic Safety-Aware CLIP, employs entailment loss functions to model the
hierarchical and asymmetrical relations between safe and unsafe image-text
pairs. This modelling, ineffective in standard vision-language models due to
their reliance on Euclidean embeddings, endows the model with awareness of
unsafe content, enabling it to serve as both a multimodal unsafe classifier and
a flexible content retriever, with the option to dynamically redirect unsafe
queries toward safer alternatives or retain the original output. Extensive
experiments show that our approach not only enhances safety recognition but
also establishes a more adaptable and interpretable framework for content
moderation in vision-language models. Our source code is available at
https://github.com/aimagelab/HySAC.