In image-based authentication systems designed to protect physical assets, the lighting conditions under which images are captured typically remain consistent. Therefore, if an image captured under different lighting conditions is input into the system, it can be considered an indication of unauthorized access. This paper presents a system to discriminate whether the image used for authentication and the image registered in the authentication database is captured under differing lighting conditions using hyperspectral imaging. Specifically, we construct a dataset for this task and train a feature extractor for the identification based on metric learning, enabling robust and accurate discrimination of lighting conditions.