Outside the Box: Abstraction-Based Monitoring of Neural Networks

TL;DR

This paper introduces an abstraction-based framework for monitoring neural networks at runtime by analyzing hidden layer behaviors to detect novel inputs, addressing limitations of confidence-based methods.

Contribution

It presents a novel approach using program analysis abstractions to monitor neural networks' hidden layers for improved novelty detection.

Findings

Effective detection of novel inputs on image classification benchmarks.

Robustness to variability in unknown classes.

Flexible trade-off between false warnings and detection accuracy.

Abstract

Neural networks have demonstrated unmatched performance in a range of classification tasks. Despite numerous efforts of the research community, novelty detection remains one of the significant limitations of neural networks. The ability to identify previously unseen inputs as novel is crucial for our understanding of the decisions made by neural networks. At runtime, inputs not falling into any of the categories learned during training cannot be classified correctly by the neural network. Existing approaches treat the neural network as a black box and try to detect novel inputs based on the confidence of the output predictions. However, neural networks are not trained to reduce their confidence for novel inputs, which limits the effectiveness of these approaches. We propose a framework to monitor a neural network by observing the hidden layers. We employ a common abstraction from program analysis - boxes - to identify novel behaviors in the monitored layers, i.e., inputs that cause behaviors outside the box. For each neuron, the boxes range over the values seen in training. The framework is efficient and flexible to achieve a desired trade-off between raising false warnings and detecting novel inputs. We illustrate the performance and the robustness to variability in the unknown classes on popular image-classification benchmarks.