Poster
in
Workshop: Principled Design for Trustworthy AI: Interpretability, Robustness, and Safety Across Modalities

OmniPatch: A Universal Adversarial Patch for ViT-CNN Cross-Architecture Transfer in Semantic Segmentation

Aarush Aggarwal ⋅ Akshat Tomar ⋅ Amritanshu Tiwari ⋅ Sargam Goyal

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Abstract

Robust semantic segmentation is crucial for safe autonomous driving, yet deployed models remain vulnerable to black-box adversarial attacks when target weights are unknown. Most existing approaches either craft image-wide perturbations or optimize patches for a single architecture, which limits their practicality and transferability. We introduce $\textbf{OmniPatch}$, a training framework for learning a $\textit{universal adversarial patch}$ that generalizes across images and both ViT and CNN architectures without requiring access to target model parameters.