<p>Robot vision systems are integral to the autonomous functioning of robots, enabling tasks such as object recognition, navigation, and interaction with the environment. Nonetheless, these systems are highly prone to data poisoning and adversarial attacks, which can undermine their effectiveness and reliability. This paper investigates the relationship between these two types of attacks, with a particular focus on their similarities in feature space distribution and sensitivity to mutations in robot vision models. By enhancing existing adversarial example detection methods, we make them more effective at defending against data poisoning attacks in robot vision systems. Experimental results show that our improved defense methods not only protect against various types of data poisoning attacks but often outperform techniques specifically designed for such attacks, significantly enhancing the robustness and security of robot vision systems in real-world scenarios.</p>