---
title: "Semantic Robustness Certification for Vision-Language Models (Raw)"
created: 2026-07-04
type: raw-paper
source: "arXiv:2606.18839"
venue: "ICML 2026"
authors: ["Peiyu Yang", "Paul Montague", "Feng Liu", "Andrew C. Cullen", "Amardeep Kaur", "Christopher Leckie", "Sarah M. Erfani"]
---

# Semantic Robustness Certification for Vision-Language Models

- **arXiv**: 2606.18839
- **Venue**: ICML 2026
- **Institutions**: University of Melbourne, Defence Science and Technology Group
- **Code**: https://github.com/ypeiyu/vlm-semantic-cert

## Abstract

Vision-language models (VLMs) are now widely used in downstream tasks. However, real-world applications often expose VLMs to distribution shifts induced by semantic variation (e.g., shape, size, and style). Robustness certification determines if a model's prediction changes when transformations are applied to its input. While most certification frameworks study geometric or pixel-level transformations over inputs, this work proposes a novel framework that enables certifying VLM robustness under semantic-level transformations. Leveraging the open-vocabulary capability of VLMs, we use text prompts as semantic proxies to construct transformations parameterized by an extent that controls the degree of semantic variation. By characterizing the VLM decision boundary in closed form, our framework quantitatively certifies extent intervals for which the predicted class remains unchanged under the semantic transformation. Our framework is the first to certify VLM robustness under semantic-level variations without requiring additional data for each variation, making it practical to apply.

## Key Contributions

1. Text prompts as semantic proxies to formalize semantic transformations for VLMs
2. Closed-form characterization of VLM decision boundary → precise prediction-invariant intervals
3. Evaluations on both synthetic and real-world data — transformations align with target semantics, certificates match prediction changes
