In computer vision and robotics, ensuring that AI systems remain reliable under real-world conditions is a growing challenge. Deep neural network (DNN)-based vision systems are increasingly used in safety-critical applications such as autonomous driving, where misinterpreting a traffic sign could lead to unsafe decisions. Everyday wear and tear can subtly alter traffic signs, raising questions about whether naturally occurring damage could also expose vulnerabilities in AI-based recognition systems.
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