Mitigating Spurious Negative Pairs for Robust Industrial Anomaly Detection

TL;DR

This paper proposes a novel adversarial training method for industrial anomaly detection that addresses spurious negative pairs, leading to significantly improved robustness against adversarial attacks in critical applications.

Contribution

It introduces a pseudo-anomaly group and a contrastive loss-based adversarial training approach that mitigates spurious negative pairs, enhancing robustness in anomaly detection.

Findings

Achieved 26.1% improvement in robust detection across benchmarks.

Demonstrated effectiveness in both clean and adversarial scenarios.

Proposed a new method to handle spurious negative pairs in contrastive learning.

Abstract

Despite significant progress in Anomaly Detection (AD), the robustness of existing detection methods against adversarial attacks remains a challenge, compromising their reliability in critical real-world applications such as autonomous driving. This issue primarily arises from the AD setup, which assumes that training data is limited to a group of unlabeled normal samples, making the detectors vulnerable to adversarial anomaly samples during testing. Additionally, implementing adversarial training as a safeguard encounters difficulties, such as formulating an effective objective function without access to labels. An ideal objective function for adversarial training in AD should promote strong perturbations both within and between the normal and anomaly groups to maximize margin between normal and anomaly distribution. To address these issues, we first propose crafting a pseudo-anomaly group derived from normal group samples. Then, we demonstrate that adversarial training with contrastive loss could serve as an ideal objective function, as it creates both inter- and intra-group perturbations. However, we notice that spurious negative pairs compromise the conventional contrastive loss to achieve robust AD. Spurious negative pairs are those that should be closely mapped but are erroneously separated. These pairs introduce noise and misguide the direction of inter-group adversarial perturbations. To overcome the effect of spurious negative pairs, we define opposite pairs and adversarially pull them apart to strengthen inter-group perturbations. Experimental results demonstrate our superior performance in both clean and adversarial scenarios, with a 26.1% improvement in robust detection across various challenging benchmark datasets. The implementation of our work is available at: https://github.com/rohban-lab/COBRA.