Deep learning-based imperceptible adversarial attack detection methods have recently seen significant progress. However, the accuracy, latency, and computational cost of previous methods remain insufficient. Particularly, trained attack detection models can potentially be applied in previously unseen conditions, such as new datasets or attacks for real-world applications. Therefore, to improve domain generalization performance, we propose a new method for cross-domain imperceptible adversarial attack detection by leveraging domain generalization, where we train the model's feature extractor or detector with a partner well-tuned for different domains. Different from conventional domain generalization methods, we use the global loss and local loss to train each feature extractor or detector. Moreover, to efficiently re-use high-resolution feature maps from the feature extractor, we propose a feature fusion network, which exploits feature maps from images that are attacked with different error rates and helps extract rich features to further improve the attack detection accuracy. Extensive experiments on four public datasets are used to demonstrate the efficacy of the proposed method.