OmniPatch: A Universal Adversarial Patch for ViT-CNN Cross-Architecture Transfer in Semantic Segmentation
arXiv:2603.20777v1 Announce Type: new 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 OmniPatch, a training framework for learning a universal adversarial patch that generalizes across images and both ViT and CNN architectures without requiring access to target model parameters.
arXiv:2603.20777v1 Announce Type: new 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 OmniPatch, a training framework for learning a universal adversarial patch that generalizes across images and both ViT and CNN architectures without requiring access to target model parameters.
Executive Summary
This article proposes OmniPatch, a universal adversarial patch framework that enables robust semantic segmentation across various images and architectures (ViT and CNN) without requiring target model parameters. OmniPatch is trained to generalize across different images and architectures, mitigating the limitations of existing approaches that focus on image-wide perturbations or single-architecture patches. The framework's ability to learn a universal patch enhances transferability and practicality in real-world applications, such as safe autonomous driving. However, the article's scope is limited to semantic segmentation, and its implications for broader applications of adversarial patches are not fully explored. Nevertheless, OmniPatch presents a promising solution for enhancing robustness in image processing tasks.
Key Points
- ▸ OmniPatch learns a universal adversarial patch for semantic segmentation
- ▸ Generalizes across images and both ViT and CNN architectures
- ▸ Does not require access to target model parameters
Merits
Strength
OmniPatch addresses the limitations of existing approaches by learning a universal patch, enhancing transferability and practicality in real-world applications.
Demerits
Limitation
The article's scope is limited to semantic segmentation, and its implications for broader applications of adversarial patches are not fully explored.
Limitation
The framework's robustness against various types of attacks and its performance on diverse datasets are not thoroughly evaluated.
Expert Commentary
OmniPatch presents a promising solution for enhancing robustness in semantic segmentation tasks. However, the article's scope is limited, and its implications for broader applications of adversarial patches are not fully explored. Future research should focus on evaluating the framework's robustness against various types of attacks and its performance on diverse datasets. Additionally, the development of OmniPatch raises important questions about the need for regulatory frameworks that address the potential risks and consequences of deploying vulnerable AI models in critical applications.
Recommendations
- ✓ Future research should explore the application of OmniPatch in other computer vision tasks and its potential extensions to other architectures and datasets.
- ✓ Developers and policymakers should consider the implications of deploying vulnerable AI models in critical applications and work towards establishing regulatory frameworks that address these risks.
Sources
Original: arXiv - cs.LG
