Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns. As collecting representative examples of all possible anomalies is infeasible, we tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs. To this end, we introduce a corruption model that injects artificial disruptions into training images to mimic structural defects. While reminiscent of denoising autoencoders, our approach differs in two key aspects. First, instead of unstructured i.i.d.\ noise, we apply structured, spatially coherent perturbations that make the task a hybrid of segmentation and inpainting. Second, and counterintuitively, we add and preserve Gaussian noise on top of the occlusions, which acts as a Tikhonov regularizer anchoring the Jacobian of the reconstruction function toward identity. This identity-anchored regularization stabilizes reconstruction and further improves both detection and segmentation accuracy. On the MVTec AD benchmark, our method achieves state-of-the-art results (I/P-AUROC: 99.9/99.4), supporting our theoretical framework and demonstrating its practical relevance for automatic inspection.

翻译：异常检测在自动化工业检测中发挥着关键作用，其目标是在均匀视觉模式中识别细微或罕见的缺陷。由于收集所有可能异常的代表性样本不可行，我们采用自监督自动编码器学习修复受损输入，以解决结构异常检测问题。为此，我们引入一种损坏模型，将人工干扰注入训练图像以模拟结构缺陷。虽然与去噪自动编码器类似，但我们的方法在两个方面存在关键差异。首先，我们采用结构化、空间连贯的扰动而非非独立同分布的无结构噪声，使任务兼具分割与修复特性。其次，反直觉地，我们在遮挡区域上添加并保留高斯噪声，其作为Tikhonov正则化器，将重构函数的雅可比矩阵锚定于恒等映射。这种基于恒等映射的正则化稳定了重构过程，并进一步提升了检测与分割精度。在MVTec AD基准测试中，我们的方法取得了最先进的结果（图像/像素级AUROC：99.9/99.4），验证了理论框架的有效性并证明了其在自动检测中的实用价值。