mlsanityData to 14 Oct 2026

Adversarial Attacks and Defences Competition

arXiv 1804.00097 ↗First posted 31 Mar 2018cs.CV cs.CR cs.LGcited by 96

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Abstract (arXiv)

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.

Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2018.

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Citing papers per year: 2021: 25; 2022: 22; 2023: 24; 2024: 11; 2025: 9; 2026: 5.

96 papers.

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  1. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,420
  2. 2023Better Diffusion Models Further Improve Adversarial Trainingcited by 208
  3. 2022Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shiftcited by 94
  4. 2023Diffusion Models for Imperceptible and Transferable Adversarial Attackcited by 94
  5. 2022Improving Adversarial Transferability via Neuron Attribution-Based Attackscited by 93
  6. 2021No One Representation to Rule Them All: Overlapping Features of Training Methodscited by 58
  7. 2021Meta Gradient Adversarial Attackcited by 55
  8. 2022Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Inputcited by 50
  9. 2023Improving the Transferability of Adversarial Samples by Path-Augmented Methodcited by 36
  10. 2022Transferable Adversarial Attack based on Integrated Gradientscited by 35
  11. 2021Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networkscited by 32
  12. 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
38 more
  1. 2022Overparameterized Linear Regression under Adversarial Attackscited by 21
  2. 2022PRoA: A Probabilistic Robustness Assessment against Functional Perturbationscited by 19
  3. 2024MirrorCheck: Efficient Adversarial Defense for Vision-Language Modelscited by 19
  4. 2023Regularization properties of adversarially-trained linear regressioncited by 18
  5. 2022Realistic Full-Body Anonymization with Surface-Guided GANscited by 15
  6. 2025Lifelong Safety Alignment for Language Modelscited by 15
  7. 2021Learning to Learn Transferable Attackcited by 13
  8. 2022Blackbox Attacks via Surrogate Ensemble Searchcited by 13
  9. 2023Black-box Targeted Adversarial Attack on Segment Anything (SAM)cited by 13
  10. 2022Robust Reinforcement Learning via Genetic Curriculumcited by 10
  11. 2022Invariant Learning via Diffusion Dreamed Distribution Shiftscited by 10
  12. 2025Transferable Adversarial Attacks on Black-Box Vision-Language Modelscited by 10
  13. 2021A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Trackingcited by 9
  14. 2021AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learningcited by 9
  15. 2022Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attackscited by 7
  16. 2023Adversarial Attack with Raindropscited by 7
  17. 2023Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perceptioncited by 7
  18. 2024Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Sizecited by 7
  19. 2023Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Makingcited by 6
  20. 2023LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competitioncited by 6
  21. 2023SoK: Pitfalls in Evaluating Black-Box Attackscited by 6
  22. 2025NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusioncited by 6
  23. 2026Pushing the Frontier of Black-Box LVLM Attacks via Fine-Grained Detail Targetingcited by 6
  24. 2021Light Lies: Optical Adversarial Attackcited by 5
  25. 2021A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classificationcited by 5
  26. 2022Software Testing for Machine Learningcited by 5
  27. 2023Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domaincited by 5
  28. 2021MISA: Online Defense of Trojaned Models using Misattributionscited by 4
  29. 2021Impact of Attention on Adversarial Robustness of Image Classification Modelscited by 4
  30. 2021Moir'e Attack (MA): A New Potential Risk of Screen Photoscited by 4
  31. 2021Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Networkcited by 4
  32. 2021Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networkscited by 4
  33. 2022Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defensecited by 4
  34. 2023Backpropagation Path Search On Adversarial Transferabilitycited by 4
  35. 2021Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagescited by 3
  36. 2021Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?cited by 3
  37. 2021Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Frameworkcited by 3
  38. 2022Hindering Adversarial Attacks with Implicit Neural Representationscited by 3

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