Towards Adversarial Robustness and Uncertainty Quantification in DINOv2-based Few-Shot Anomaly Detection

• URL: http://arxiv.org/abs/2510.13643v1
• Date: Wed, 15 Oct 2025 15:06:45 GMT
• Title: Towards Adversarial Robustness and Uncertainty Quantification in DINOv2-based Few-Shot Anomaly Detection
• Authors: Akib Mohammed Khan, Bartosz Krawczyk,
• Abstract summary: Foundation models such as DINOv2 have shown strong performance in few-shot anomaly detection.<n>We present one of the first systematic studies of adversarial attacks and uncertainty estimation in this setting.<n>We find that raw anomaly scores are poorly calibrated, revealing a gap between confidence and correctness that limits safety-critical use.
• Score: 6.288045889067255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models such as DINOv2 have shown strong performance in few-shot anomaly detection, yet two key questions remain unexamined: (i) how susceptible are these detectors to adversarial perturbations; and (ii) how well do their anomaly scores reflect calibrated uncertainty? Building on AnomalyDINO, a training-free deep nearest-neighbor detector over DINOv2 features, we present one of the first systematic studies of adversarial attacks and uncertainty estimation in this setting. To enable white-box gradient attacks while preserving test-time behavior, we attach a lightweight linear head to frozen DINOv2 features only for crafting perturbations. Using this heuristic, we evaluate the impact of FGSM across the MVTec-AD and VisA datasets and observe consistent drops in F1, AUROC, AP, and G-mean, indicating that imperceptible perturbations can flip nearest-neighbor relations in feature space to induce confident misclassification. Complementing robustness, we probe reliability and find that raw anomaly scores are poorly calibrated, revealing a gap between confidence and correctness that limits safety-critical use. As a simple, strong baseline toward trustworthiness, we apply post-hoc Platt scaling to the anomaly scores for uncertainty estimation. The resulting calibrated posteriors yield significantly higher predictive entropy on adversarially perturbed inputs than on clean ones, enabling a practical flagging mechanism for attack detection while reducing calibration error (ECE). Our findings surface concrete vulnerabilities in DINOv2-based few-shot anomaly detectors and establish an evaluation protocol and baseline for robust, uncertainty-aware anomaly detection. We argue that adversarial robustness and principled uncertainty quantification are not optional add-ons but essential capabilities if anomaly detection systems are to be trustworthy and ready for real-world deployment.

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