This paper explores the utility of diffusion-based models for anomaly detection, focusing on their efficacy in identifying deviations in both compact and high-resolution datasets. Diffusion-based architectures, including Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs), are evaluated for their performance using reconstruction objectives. By leveraging the strengths of these models, this study benchmarks their performance against traditional anomaly detection methods such as Isolation Forests, One-Class SVMs, and COPOD. The results demonstrate the superior adaptability, scalability, and robustness of diffusion-based methods in handling complex real-world anomaly detection tasks. Key findings highlight the role of reconstruction error in enhancing detection accuracy and underscore the scalability of these models to high-dimensional datasets. Future directions include optimizing encoder-decoder architectures and exploring multi-modal datasets to further advance diffusion-based anomaly detection.

翻译：本文探讨了基于扩散的模型在异常检测中的应用价值，重点关注其在紧凑数据集和高分辨率数据集中识别偏差的有效性。研究评估了包括去噪扩散概率模型（DDPMs）和扩散Transformer（DiTs）在内的扩散架构在重建目标下的性能表现。通过利用这些模型的优势，本研究将其性能与孤立森林、单类支持向量机和COPOD等传统异常检测方法进行了基准比较。结果表明，基于扩散的方法在处理复杂现实世界异常检测任务时具有卓越的适应性、可扩展性和鲁棒性。关键发现揭示了重建误差在提升检测精度中的作用，并强调了这些模型对高维数据集的可扩展性。未来研究方向包括优化编码器-解码器架构以及探索多模态数据集，以进一步推进基于扩散的异常检测技术。