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Evaluating Adversarial Robustness with Expected Viable Performance

arXiv 2309.09928 ↗First posted 18 Sep 2023cs.LGcited by 0

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Abstract (arXiv)

We introduce a metric for evaluating the robustness of a classifier, with particular attention to adversarial perturbations, in terms of expected functionality with respect to possible adversarial perturbations. A classifier is assumed to be non-functional (that is, has a functionality of zero) with respect to a perturbation bound if a conventional measure of performance, such as classification accuracy, is less than a minimally viable threshold when the classifier is tested on examples from that perturbation bound. Defining robustness in terms of an expected value is motivated by a domain general approach to robustness quantification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 18 Sep 2023.

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References · 14

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2018Robustness May Be at Odds with Accuracycited by 880
  4. 2019On Evaluating Adversarial Robustnesscited by 447
  5. 2018Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approachcited by 182
  6. 2018Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Modelscited by 135
  7. 2022Robustness and Accuracy Could Be Reconcilable by (Proper) Definitioncited by 111
  8. 2019On the Connection Between Adversarial Robustness and Saliency Map Interpretabilitycited by 80
  9. 2019Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifierscited by 65
  10. 2020Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbationscited by 55
  11. 2021SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluationcited by 16
  12. 2019Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustnesscited by 14
2 more
  1. 2018Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Sizecited by 9
  2. 2023Overcoming Adversarial Attacks for Human-in-the-Loop Applicationscited by 1

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