Hyperbolic Safety-Aware Vision-Language Models
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.
67d95fa8fb17ef1c744db2db
Tobia Poppi
claimed_verified
2025-03-18T14:57:56.046Z
64ee11c125d2bb76c06e243d
https://cdn-avatars.huggingface.co/v1/production/uploads/64ee11c125d2bb76c06e243d/DR2NV4NYINtpRoMd0NupA.png
Tobia Poppi
user
tobi1modna
67d95fa8fb17ef1c744db2dc
Tejaswi Kasarla
admin_assigned
2025-03-19T13:46:42.945Z
67a4de92a41dcedc1632eb43
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/rKpXABi8RqSeRh2RXBlZU.png
Tejaswi Kasarla
user
tkasarla
67d95fa8fb17ef1c744db2dd
Pascal Mettes
67d95fa8fb17ef1c744db2de
Lorenzo Baraldi
67d95fa8fb17ef1c744db2df
Rita Cucchiara
Hyperbolic Safety-Aware Vision-Language Models
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.
Hyperbolic Safety-Aware CLIP (HySAC) enhances safety in vision-language models by representing safe and unsafe content in hyperbolic space using entailment loss functions, allowing it to classify unsafe content and redirect queries dynamically.
hyperbolic space entailment hierarchy entailment loss functions multimodal unsafe classifier content retriever dynamic redirection safety recognition content moderation