mlsanityData to 14 Oct 2026

Quantum-Inspired Analysis of Neural Network Vulnerabilities: The Role of Conjugate Variables in System Attacks

arXiv 2402.10983 ↗First posted 16 Feb 2024cs.LG cs.CR quant-phcited by 4

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Abstract (arXiv)

Neural networks demonstrate inherent vulnerability to small, non-random perturbations, emerging as adversarial attacks. Such attacks, born from the gradient of the loss function relative to the input, are discerned as input conjugates, revealing a systemic fragility within the network structure. Intriguingly, a mathematical congruence manifests between this mechanism and the quantum physics' uncertainty principle, casting light on a hitherto unanticipated interdisciplinarity. This inherent susceptibility within neural network systems is generally intrinsic, highlighting not only the innate vulnerability of these networks but also suggesting potential advancements in the interdisciplinary area for understanding these black-box networks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 31 of its 52 references that are in the map. First posted on arXiv 16 Feb 2024.

Builds on

31 of 52 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2013Intriguing properties of neural networkscited by 5,155
  5. 2016Adversarial examples in the physical worldcited by 1,769
  6. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,517
  7. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  8. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  9. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  10. 2018Robustness May Be at Odds with Accuracycited by 880
  11. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  12. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 685
19 more
  1. 2017One pixel attack for fooling deep neural networkscited by 582
  2. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 571
  3. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 498
  4. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 274
  5. 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 212
  6. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 194
  7. 2020Randomized Smoothing of All Shapes and Sizescited by 147
  8. 2018Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Modelscited by 135
  9. 2021Globally-Robust Neural Networkscited by 108
  10. 2018On the importance of single directions for generalizationcited by 99
  11. 2018SparseFool: a few pixels make a big differencecited by 94
  12. 2021A Survey On Universal Adversarial Attackcited by 59
  13. 2021Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problemcited by 53
  14. 2020Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracycited by 45
  15. 2021A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attackscited by 34
  16. 2022GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothingcited by 23
  17. 2023GenPhys: From Physical Processes to Generative Modelscited by 19
  18. 2016Are Accuracy and Robustness Correlated?cited by 11
  19. 2020Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networkscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV Networkcited by 0
  2. 2025Phase-space entropy at acquisition reflects downstream learnabilitycited by 0
  3. 2026A Geometric Probe of the Accuracy-Robustness Trade-off: Sharp Boundaries in Symmetry-Breaking Dimensional Expansioncited by 0
  4. 2026Hardware-Friendly Input Expansion for Accelerating Function Approximationcited by 0