Research map / Adversarial robustness and security / Attacks on text and graphs

Adversarially Robust Anomaly Detection through Spurious Negative Pair Mitigation

ICLR 2025 · Adversarial robustness and security › Attacks on text and graphs · approach: Defenses against universal perturbations

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes a method for adversarially robust anomaly detection that mitigates spurious negative pairs during contrastive training, as stated in the title; only the abstract was available.

Problem

Anomaly detection methods trained only on unlabeled normal samples are vulnerable to adversarial attacks, which undermines their reliability in critical applications such as autonomous driving.

Approach

Addresses the adversarial vulnerability of AD by mitigating spurious negative pairs in the training objective; the exact mechanism is not described in the available abstract.

Closest papers by meaning

• Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data (ICML 2024 · similarity 0.88)
• PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies (CVPR 2025 · similarity 0.88)
• FrameShield: Adversarially Robust Video Anomaly Detection (NeurIPS 2025 · similarity 0.87)
• Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data (ICLR 2025 · similarity 0.87)
• One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy (ICML 2025 · similarity 0.86)
• RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples (ICML 2024 · similarity 0.86)