mlsanityData to 7 Oct 2026

Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks

arXiv 2608.13296 ↗First posted 13 Aug 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization methods. We argue that the tasks related to the black-box adversarial attack (BBAA) can serve as valuable global optimization benchmark in many-dimensional space. We demonstrate the efficiency of several types of evolutionary algorithms and other metaheuristics in solving example BBAA problems. Thus, we take a step towards convergence of global optimization methods to the challenges and needs that arise in the modern machine learning field.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 13 Aug 2026.

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References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  7. 2013Intriguing properties of neural networkscited by 5,114
  8. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  9. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  10. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 682
  11. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  12. 2017One pixel attack for fooling deep neural networkscited by 579
15 more
  1. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 530
  2. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
  3. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  4. 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
  5. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 328
  6. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
  7. 2018Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priorscited by 145
  8. 2018AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networkscited by 137
  9. 2018GenAttack: Practical Black-box Attacks with Gradient-Free Optimizationcited by 82
  10. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 65
  11. 2018Adversarial Attacks Against Medical Deep Learning Systemscited by 61
  12. 2023BlackboxBench: A Comprehensive Benchmark of Black-box Adversarial Attackscited by 21
  13. 2019COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suitecited by 12
  14. 2019They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimizationcited by 4
  15. 2021EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attackscited by 4

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