  The detection and the quantification of anomalies in image data are critical
tasks in industrial scenes such as detecting micro scratches on product. In
recent years, due to the difficulty of defining anomalies and the limit of
correcting their labels, research on unsupervised anomaly detection using
generative models has attracted attention. Generally, in those studies, only
normal images are used for training to model the distribution of normal images.
The model measures the anomalies in the target images by reproducing the most
similar images and scoring image patches indicating their fit to the learned
distribution. This approach is based on a strong presumption; the trained model
should not be able to generate abnormal images. However, in reality, the model
can generate abnormal images mainly due to noisy normal data which include
small abnormal pixels, and such noise severely affects the accuracy of the
model. Therefore, we propose a novel anomaly detection method to distort the
distribution of the model with existing abnormal images. The proposed method
detects pixel-level micro anomalies with a high accuracy from 1024x1024 high
resolution images which are actually used in an industrial scene. In this
paper, we share experimental results on open datasets, due to the
confidentiality of the data.
