  This study targets Multi-Lighting Image Anomaly Detection (MLIAD), where
multiple lighting conditions are utilized to enhance imaging quality and
anomaly detection performance. While numerous image anomaly detection methods
have been proposed, they lack the capacity to handle multiple inputs for a
single sample, like multi-lighting images in MLIAD. Hence, this study proposes
Attention Fusion Reverse Distillation (AFRD) to handle multiple inputs in
MLIAD. For this purpose, AFRD utilizes a pre-trained teacher network to extract
features from multiple inputs. Then these features are aggregated into fused
features through an attention module. Subsequently, a corresponding student
net-work is utilized to regress the attention fused features. The regression
errors are denoted as anomaly scores during inference. Experiments on
Eyecandies demonstrates that AFRD achieves superior MLIAD performance than
other MLIAD alternatives, also highlighting the benefit of using multiple
lighting conditions for anomaly detection.
