Adversarial Weather

Adversarial weather research explores how realistic weather phenomena, such as rain, snow, and fog, can be manipulated to create conditions that degrade the performance of computer vision systems, particularly those used in autonomous vehicles and robotics. Current research focuses on generating realistic, yet adversarial, weather effects using differentiable rendering and generative adversarial networks (GANs) to create training data that improves system robustness. This work is significant because it highlights vulnerabilities in existing systems and provides methods for improving their resilience to real-world environmental challenges, ultimately contributing to safer and more reliable autonomous systems.