title: Hyperbolic Safety-Aware Vision-Language Models
summary: 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.
paper_authors._id: 67d95fa8fb17ef1c744db2db
paper_authors.name: Tobia Poppi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T14:57:56.046Z
paper_authors._id: 64ee11c125d2bb76c06e243d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ee11c125d2bb76c06e243d/DR2NV4NYINtpRoMd0NupA.png
paper_authors.fullname: Tobia Poppi
paper_authors.type: user
paper_authors.user: tobi1modna
paper_authors._id: 67d95fa8fb17ef1c744db2dc
paper_authors.name: Tejaswi Kasarla
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:46:42.945Z
paper_authors._id: 67a4de92a41dcedc1632eb43
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/rKpXABi8RqSeRh2RXBlZU.png
paper_authors.fullname: Tejaswi Kasarla
paper_authors.type: user
paper_authors.user: tkasarla
paper_authors._id: 67d95fa8fb17ef1c744db2dd
paper_authors.name: Pascal Mettes
paper_authors._id: 67d95fa8fb17ef1c744db2de
paper_authors.name: Lorenzo Baraldi
paper_authors._id: 67d95fa8fb17ef1c744db2df
paper_authors.name: Rita Cucchiara
paper_title: Hyperbolic Safety-Aware Vision-Language Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: hyperbolic space entailment hierarchy entailment loss functions multimodal unsafe classifier content retriever dynamic redirection safety recognition content moderation