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PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies
- Anomaly Detection (AD) and Anomaly Localization (AL) are critical in high-reliability fields like medical imaging and industrial monitoring.
- Current AD and AL methods are vulnerable to adversarial attacks due to limited training data consisting mainly of normal, unlabeled samples.
- PatchGuard is introduced as an adversarially robust AD and AL technique that incorporates pseudo anomalies and localization masks within a Vision Transformer (ViT) architecture to address these vulnerabilities.
- The study explores the essential features of pseudo anomalies and provides theoretical insights into attention mechanisms required to enhance the adversarial robustness of AD and AL systems.
- The approach leverages Foreground-Aware Pseudo-Anomalies to improve anomaly-aware methods and integrates them into a ViT-based framework.
- Adversarial training is guided by a novel loss function aimed at enhancing model robustness, as supported by theoretical analysis.
- Experimental results on established industrial and medical datasets show that PatchGuard surpasses previous methods in adversarial scenarios with significant performance gains of 53.2% in AD and 68.5% in AL, while maintaining competitive accuracy in non-adversarial settings.
- The code repository for PatchGuard is available at https://github.com/rohban-lab/PatchGuard
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