  Conventional anomaly detection techniques based on reconstruction via
denoising diffusion model are widely used due to their ability to identify
anomaly locations and shapes with high performance. However, there is a
limitation in determining appropriate noise parameters that can degrade
anomalies while preserving normal characteristics. Also, due to the volatility
of the diffusion model, normal regions can fluctuate considerably during
reconstruction, resulting in false detection. In this paper, we propose a
method to detect anomalies by analysis of reconstruction trend depending on the
degree of degradation, effectively solving the both problems of existing
methods. The proposed method is validated on an open dataset for industrial
anomaly detection, improving the performance of existing methods on a number of
evaluation criteria. With the ease of combination with existing anomaly
detection methods, it provides a tradeoff between computational cost and
performance, allowing it high application potential in manufacturing industry.
