[Paper Review] Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles

Xingyu Zhao, Wei Huang+5 more|arXiv (Cornell University)|Jun 2, 2021

Adversarial Robustness in Machine Learning29 references4 citations

TL;DR

This paper proposes a model-agnostic reliability assessment method for deep learning classifiers by combining operational profile (OP) data with robustness evaluation across partitioned input spaces. It estimates the probability of misclassification per input (pmi) with confidence levels, offering a rigorous, evidence-based framework that integrates robustness and usage patterns for improved dependability assessment in safety-critical applications.

ABSTRACT

The utilisation of Deep Learning (DL) is advancing into increasingly more sophisticated applications. While it shows great potential to provide transformational capabilities, DL also raises new challenges regarding its reliability in critical functions. In this paper, we present a model-agnostic reliability assessment method for DL classifiers, based on evidence from robustness evaluation and the operational profile (OP) of a given application. We partition the input space into small cells and then "assemble" their robustness (to the ground truth) according to the OP, where estimators on the cells' robustness and OPs are provided. Reliability estimates in terms of the probability of misclassification per input (pmi) can be derived together with confidence levels. A prototype tool is demonstrated with simplified case studies. Model assumptions and extension to real-world applications are also discussed. While our model easily uncovers the inherent difficulties of assessing the DL dependability (e.g. lack of data with ground truth and scalability issues), we provide preliminary/compromised solutions to advance in this research direction.

Motivation & Objective

• To address the critical challenge of assessing deep learning (DL) reliability in safety-critical systems where traditional verification methods fall short.
• To develop a model-agnostic reliability assessment method that integrates both operational profile (OP) data and robustness evidence from input space partitioning.
• To provide a statistically grounded estimate of the probability of misclassification per input (pmi) with associated confidence levels.
• To identify and discuss inherent difficulties in DL dependability assessment, such as data scarcity and scalability, while proposing preliminary solutions.
• To demonstrate the feasibility of the approach via a prototype tool and simplified case studies.

Proposed method

• The input space is partitioned into small cells around each test input to enable localized robustness evaluation.
• Robustness is evaluated within norm-balls (e.g., ℓp-norm) around each input, using adversarial attack techniques to determine the minimal perturbation that causes misclassification.
• The operational profile (OP) is used to weight the contribution of each cell’s robustness to the overall reliability estimate, reflecting real-world usage frequency.
• A reliability estimate (pmi) is computed as a weighted average of cell-level misclassification probabilities, derived from robustness and OP data.
• Confidence intervals for the pmi estimate are derived using bootstrapping techniques applied to the operational profile and robustness estimators.
• The method is implemented in a prototype tool available on GitHub, enabling end-to-end reliability assessment for pretrained DL models.

Experimental results

Research questions

• RQ1 How can robustness evidence and operational profile data be combined to produce a reliable, model-agnostic estimate of deep learning classifier misclassification probability?
• RQ2 What is the impact of input space partitioning and local robustness evaluation on the accuracy and confidence of reliability estimates?
• RQ3 How can confidence levels be assigned to reliability estimates derived from limited ground-truth data and adversarial robustness evaluations?
• RQ4 What are the inherent challenges in assessing DL dependability, and how can they be mitigated with practical, preliminary solutions?
• RQ5 To what extent does the proposed method improve upon naive accuracy-based evaluation in capturing real-world reliability?

Key findings

• The proposed method successfully estimates the probability of misclassification per input (pmi) with associated confidence levels, providing a rigorous reliability metric for DL classifiers.
• The integration of operational profile and robustness evidence leads to a more trustworthy reliability assessment than accuracy alone, especially in safety-critical contexts.
• The prototype tool demonstrates the feasibility of the approach on simplified case studies, validating the core methodology.
• The method reveals key challenges in DL dependability assessment, including data scarcity for ground truth and scalability issues in robustness evaluation.
• Bootstrapping is effectively used to estimate variance and confidence intervals for the pmi estimate, enhancing the statistical robustness of the results.
• The approach is shown to be more representative than naive averaging of test set robustness, as it accounts for both usage frequency (OP) and local robustness.