research-article

Testing DNN image classifiers for confusion & bias errors

Authors:

Vicente Ordonez,

Baishakhi RayAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

Pages 1122 - 1134

https://doi.org/10.1145/3377811.3380400

Published: 01 October 2020 Publication History

Abstract

Image classifiers are an important component of today's software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is the key catalyst behind such wide-spread success. However, wide adoption comes with serious concerns about the robustness of software systems dependent on DNNs for image classification, as several severe erroneous behaviors have been reported under sensitive and critical circumstances. We argue that developers need to rigorously test their software's image classifiers and delay deployment until acceptable. We present an approach to testing image classifier robustness based on class property violations.

We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others. These bugs usually violate some class properties of one or more of those classes. Most DNN testing techniques focus on perimage violations, so fail to detect class-level confusions or biases.

We developed a testing technique to automatically detect class-based confusion and bias errors in DNN-driven image classification software. We evaluated our implementation, DeepInspect, on several popular image classifiers with precision up to 100% (avg. 72.6%) for confusion errors, and up to 84.3% (avg. 66.8%) for bias errors. DeepInspect found hundreds of classification mistakes in widely-used models, many exposing errors indicating confusion or bias.

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Index Terms

Testing DNN image classifiers for confusion & bias errors
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

June 2020

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ISBN:9781450371216

DOI:10.1145/3377811

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North Carolina State University
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KAIST, South Korea

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Cited By

Chen JWang JZhang XSun YKwiatkowska MChen JCheng PFilkov VRay BZhou M(2024)FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature SelectionProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695472(895-906)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695472
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