mlsanityData to 7 Oct 2026

Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles

arXiv 2106.01258 ↗First posted 2 Jun 2021cs.LGcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 14 of its 57 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

14 of 57 references.

References · 14

  1. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  2. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
  3. 2016Safety Verification of Deep Neural Networkscited by 262
  4. 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
  5. 2017A Tutorial on Kernel Density Estimation and Recent Advancescited by 100
  6. 2018A Statistical Approach to Assessing Neural Network Robustnesscited by 54
  7. 2019Boosting Operational DNN Testing Efficiency through Conditioningcited by 22
  8. 2020A Safety Framework for Critical Systems Utilising Deep Neural Networkscited by 19
  9. 2019Coverage Guided Testing for Recurrent Neural Networkscited by 15
  10. 2018Probabilistic Model Checking of Robots Deployed in Extreme Environmentscited by 12
  11. 2020Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehiclescited by 9
  12. 2021Operation is the hardest teacher: estimating DNN accuracy looking for mispredictionscited by 8
2 more
  1. 2021Detecting Operational Adversarial Examples for Reliable Deep Learningcited by 8
  2. 2019Finding the Mode of a Kernel Density Estimatecited by 2

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Cited by year

Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 0; 2025: 3.

11 papers.

  1. 2022SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretabilitycited by 28
  2. 2021Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurancecited by 16
  3. 2022Hierarchical Distribution-Aware Testing of Deep Learningcited by 8
  4. 2023What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systemscited by 6
  5. 2025Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guidecited by 6
  6. 2021Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applicationscited by 5
  7. 2022Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoringcited by 4
  8. 2022Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systemscited by 2
  9. 2023Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Componentscited by 2
  10. 2025A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Modelscited by 2
  11. 2025PRBench: A Standardized Probabilistic Robustness Benchmarkcited by 1