mlsanityData to 7 Oct 2026

Improving Query Efficiency of Black-box Adversarial Attack

arXiv 2009.11508 ↗First posted 24 Sep 2020cs.LG stat.MLcited by 34

Not on the map. Why?

Abstract (arXiv)

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed via online services that only provide query outputs from inaccessible models (e.g. Google Cloud Vision API2), black-box adversarial attacks (inaccessible target model) are of critical security concerns in practice rather than white-box ones. However, existing query-based black-box adversarial attacks often require excessive model queries to maintain a high attack success rate. Therefore, in order to improve query efficiency, we explore the distribution of adversarial examples around benign inputs with the help of image structure information characterized by a Neural Process, and propose a Neural Process based black-box adversarial attack (NP-Attack) in this paper. Extensive experiments show that NP-Attack could greatly decrease the query counts under the black-box setting.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 3; 2023: 7; 2024: 8; 2025: 2; 2026: 1.

34 papers.

Sort:

By area:

  1. 2021Unlearnable Examples: Making Personal Data Unexploitablecited by 188
  2. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  3. 2024Inducing High Energy-Latency of Large Vision-Language Models with Verbose Imagescited by 69
  4. 2021Probabilistic Margins for Instance Reweighting in Adversarial Trainingcited by 57
  5. 2023Backdoor Defense via Adaptively Splitting Poisoned Datasetcited by 43
  6. 2021Boosting Fast Adversarial Training with Learnable Adversarial Initializationcited by 40
  7. 2023Improving the Transferability of Adversarial Samples by Path-Augmented Methodcited by 36
  8. 2024Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectorycited by 35
  9. 2022Prior-Guided Adversarial Initialization for Fast Adversarial Trainingcited by 34
  10. 2023Improving Fast Adversarial Training with Prior-Guided Knowledgecited by 34
  11. 2021Analysis and Applications of Class-wise Robustness in Adversarial Trainingcited by 33
  12. 2024Adversarial Robustness for Visual Grounding of Multimodal Large Language Modelscited by 23
22 more
  1. 2024Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attackcited by 18
  2. 2021A Unified Game-Theoretic Interpretation of Adversarial Robustnesscited by 17
  3. 2024Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samplescited by 14
  4. 2022Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferabilitycited by 12
  5. 2021Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attackscited by 11
  6. 2023TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentationcited by 10
  7. 2021What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Spacecited by 8
  8. 2021Protein Folding Neural Networks Are Not Robustcited by 7
  9. 2024Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMscited by 7
  10. 2023SoK: Pitfalls in Evaluating Black-Box Attackscited by 6
  11. 2021Moir'e Attack (MA): A New Potential Risk of Screen Photoscited by 4
  12. 2025Eliciting Behaviors in Multi-Turn Conversationscited by 4
  13. 2021Clustering Effect of (Linearized) Adversarial Robust Modelscited by 3
  14. 2023On the Robustness of AlphaFold: A COVID-19 Case Studycited by 2
  15. 2024Model Mimic Attack: Knowledge Distillation for Provably Transferable Adversarial Examplescited by 2
  16. 2023PRAT: PRofiling Adversarial aTtackscited by 1
  17. 2025Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Modelscited by 1
  18. 2021Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentionscited by 0
  19. 2021A Unified Game-Theoretic Interpretation of Adversarial Robustnesscited by 0
  20. 2022AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texturecited by 0
  21. 2024Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challengescited by 0
  22. 2026Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?cited by 0