Benchmarking Safety Monitors for Image Classifiers with Machine Learning

TL;DR

This paper establishes a comprehensive benchmarking framework for safety monitors in image classifiers, evaluating their effectiveness across diverse out-of-distribution scenarios, revealing current monitors' limited accuracy.

Contribution

It introduces a full pipeline framework for benchmarking ML image classifier monitors, including diverse metrics and extensive dataset evaluation, highlighting the need for improved safety mechanisms.

Findings

Monitors perform no better than random in accuracy.

Benchmarking across 79 datasets reveals limited effectiveness.

Framework enables systematic evaluation of safety monitors.

Abstract

High-accurate machine learning (ML) image classifiers cannot guarantee that they will not fail at operation. Thus, their deployment in safety-critical applications such as autonomous vehicles is still an open issue. The use of fault tolerance mechanisms such as safety monitors is a promising direction to keep the system in a safe state despite errors of the ML classifier. As the prediction from the ML is the core information directly impacting safety, many works are focusing on monitoring the ML model itself. Checking the efficiency of such monitors in the context of safety-critical applications is thus a significant challenge. Therefore, this paper aims at establishing a baseline framework for benchmarking monitors for ML image classifiers. Furthermore, we propose a framework covering the entire pipeline, from data generation to evaluation. Our approach measures monitor performance with a broader set of metrics than usually proposed in the literature. Moreover, we benchmark three different monitor approaches in 79 benchmark datasets containing five categories of out-of-distribution data for image classifiers: class novelty, noise, anomalies, distributional shifts, and adversarial attacks. Our results indicate that these monitors are no more accurate than a random monitor. We also release the code of all experiments for reproducibility.