Attention-Guided Perturbation for Unsupervised Image Anomaly Detection

Reconstruction-based methods have significantly advanced unsupervised image anomaly detection involving only normal training images. However, it has been proven that modern neural networks generally have a strong reconstruction capacity and often reconstruct both normal and abnormal samples well, thereby failing to spot anomaly regions by checking the reconstruction quality. To prevent well-reconstructed anomalies, one simple but effective strategy is to perturb normal samples and then map perturbed versions to normal ones. Yet it treats each spatial position equally, disregarding the fact that the foreground locations are inherently more important for reconstruction. Motivated by this, we present a simple yet effective reconstruction framework named Attention-Guided Perturbation Network (AGPNet), which learns to add perturbations guided with an attention mask during training. Specifically, it consists of two branches, \ie, a reconstruction branch and an auxiliary attention-based perturbation branch. The reconstruction branch learns to reconstruct normal samples, while the auxiliary one aims to produce attention masks to guide the noise perturbation process for normal samples. By doing so, we are expecting to synthesize hard yet more informative anomalies for training, which enable the reconstruction branch to learn important inherent normal patterns both comprehensively and efficiently. Extensive experiments are conducted on several popular benchmarks covering MVTec-AD, VisA, and MVTec-3D, and show that AGPNet obtains leading anomaly detection results under few-shot, one-class, and multi-class setups.

@article{cheng2025_2408.07490,
  title={ Attention-Guided Perturbation for Unsupervised Image Anomaly Detection },
  author={ Yuxuan Cheng and Tingfeng Huang and Yuxuan Cai and Jingbo Xia and Rui Yu and Jinhai Xiang and Xinwei He },
  journal={arXiv preprint arXiv:2408.07490},
  year={ 2025 }
}