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As a kind of promising biometric technology, multispectral palmprint recognition methods have attracted increasing attention in security due to their high recognition accuracy and ease of use. It is worth noting that although multispectral palmprint data contains rich complementary information, multispectral palmprint recognition methods are still vulnerable to adversarial attacks. Even if only one image of a spectrum is attacked, it can have a catastrophic impact on the recognition results. Therefore, we propose a robustness-enhanced multispectral palmprint recognition method, including a model interpretability-based adversarial detection module and a robust multispectral fusion module. Inspired by the model interpretation technology, we found there is a large difference between clean palmprint and adversarial examples after CAM visualization. Using visualized images to build an adversarial detector can lead to better detection results. Finally, the weights of clean images and adversarial examples in the fusion layer are dynamically adjusted to obtain the correct recognition results. Experiments have shown that our method can make full use of the image features that are not attacked and can effectively improve the robustness of the model.