mlsanityData to 13 Oct 2026

PopSkipJump: Decision-Based Attack for Probabilistic Classifiers

arXiv 2106.07445 ↗First posted 14 Jun 2021cs.LG cs.CR cs.CVcited by 2

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Abstract (arXiv)

Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output. Many existing attack algorithms cover various settings, from white-box to black-box classifiers, but typically assume that the answers are deterministic and often fail when they are not. We therefore propose a new adversarial decision-based attack specifically designed for classifiers with probabilistic outputs. It is based on the HopSkipJump attack by Chen et al. (2019, arXiv:1904.02144v5 ), a strong and query efficient decision-based attack originally designed for deterministic classifiers. Our P(robabilisticH)opSkipJump attack adapts its amount of queries to maintain HopSkipJump's original output quality across various noise levels, while converging to its query efficiency as the noise level decreases. We test our attack on various noise models, including state-of-the-art off-the-shelf randomized defenses, and show that they offer almost no extra robustness to decision-based attacks. Code is available at https://github.com/cjsg/PopSkipJump .

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 14 Jun 2021.

Builds on

17 of 26 references.

References · 17

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,336
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,379
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,430
  4. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,992
  5. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,336
  6. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,316
  7. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,265
  8. 2017Countering Adversarial Images using Input Transformationscited by 519
  9. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
  10. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 460
  11. 2017Mitigating Adversarial Effects Through Randomizationcited by 382
  12. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 331
5 more
  1. 2017Detecting Adversarial Samples from Artifactscited by 244
  2. 2019Accurate, reliable and fast robustness evaluationcited by 43
  3. 2019Statistical Guarantees for the Robustness of Bayesian Neural Networkscited by 35
  4. 2018Robustness Guarantees for Bayesian Inference with Gaussian Processescited by 23
  5. 2019A geometry-inspired decision-based attackcited by 22

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024A Survey on Failure Analysis and Fault Injection in AI Systemscited by 8
  2. 2023SoK: Pitfalls in Evaluating Black-Box Attackscited by 6