Can we Defend Against the Unknown? An Empirical Study About Threshold Selection for Neural Network Monitoring

TL;DR

This paper investigates how to select effective thresholds for neural network runtime monitors, especially under unforeseen threats, highlighting the importance of threshold choice in real-world safety-critical applications.

Contribution

It provides empirical insights into threshold selection challenges and explores the impact of incorporating generic threats into threshold optimization for improved robustness.

Findings

Threshold choice significantly affects monitor effectiveness against unseen threats.

Incorporating generic threats into threshold optimization can improve robustness.

Monitors trained on known threats may underperform on unforeseen threats.

Abstract

With the increasing use of neural networks in critical systems, runtime monitoring becomes essential to reject unsafe predictions during inference. Various techniques have emerged to establish rejection scores that maximize the separability between the distributions of safe and unsafe predictions. The efficacy of these approaches is mostly evaluated using threshold-agnostic metrics, such as the area under the receiver operating characteristic curve. However, in real-world applications, an effective monitor also requires identifying a good threshold to transform these scores into meaningful binary decisions. Despite the pivotal importance of threshold optimization, this problem has received little attention. A few studies touch upon this question, but they typically assume that the runtime data distribution mirrors the training distribution, which is a strong assumption as monitors are supposed to safeguard a system against potentially unforeseen threats. In this work, we present rigorous experiments on various image datasets to investigate: 1. The effectiveness of monitors in handling unforeseen threats, which are not available during threshold adjustments. 2. Whether integrating generic threats into the threshold optimization scheme can enhance the robustness of monitors.