CeTAD: Towards Certified Toxicity-Aware Distance in Vision Language Models

• URL: http://arxiv.org/abs/2503.10661v2
• Date: Fri, 21 Mar 2025 20:05:22 GMT
• Title: CeTAD: Towards Certified Toxicity-Aware Distance in Vision Language Models
• Authors: Xiangyu Yin, Jiaxu Liu, Zhen Chen, Jinwei Hu, Yi Dong, Xiaowei Huang, Wenjie Ruan,
• Abstract summary: We propose a universal certified defence framework to safeguard large vision-language models against jailbreak attacks.<n>First, we proposed a novel distance metric to quantify semantic discrepancies between malicious and intended responses.<n>Then, we devise a regressed certification approach that employs randomized smoothing to provide formal robustness guarantees.
• Score: 16.5022773312661
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in large vision-language models (VLMs) have demonstrated remarkable success across a wide range of visual understanding tasks. However, the robustness of these models against jailbreak attacks remains an open challenge. In this work, we propose a universal certified defence framework to safeguard VLMs rigorously against potential visual jailbreak attacks. First, we proposed a novel distance metric to quantify semantic discrepancies between malicious and intended responses, capturing subtle differences often overlooked by conventional cosine similarity-based measures. Then, we devise a regressed certification approach that employs randomized smoothing to provide formal robustness guarantees against both adversarial and structural perturbations, even under black-box settings. Complementing this, our feature-space defence introduces noise distributions (e.g., Gaussian, Laplacian) into the latent embeddings to safeguard against both pixel-level and structure-level perturbations. Our results highlight the potential of a formally grounded, integrated strategy toward building more resilient and trustworthy VLMs.

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