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Learning with a Strong Adversary

arXiv 1511.03034 ↗First posted 10 Nov 2015cs.LGcited by 68

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Abstract (arXiv)

The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, learning with a strong adversary, that learns robust classifiers from supervised data. The proposed method takes finding adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented and experimentally shown to be efficient. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Nov 2015.

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Citing papers per year: 2021: 27; 2022: 18; 2023: 13; 2024: 6; 2025: 4.

68 papers.

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  1. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
  2. 2021Partial success in closing the gap between human and machine visioncited by 185
  3. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 65
  4. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  5. 2021Relating Adversarially Robust Generalization to Flat Minimacited by 57
  6. 2021Constrained Learning with Non-Convex Lossescited by 42
  7. 2021Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neuronscited by 40
  8. 2021A Review of Formal Methods applied to Machine Learningcited by 33
  9. 2021Defending Against Image Corruptions Through Adversarial Augmentationscited by 28
  10. 2021The Role of Explainability in Assuring Safety of Machine Learning in Healthcarecited by 26
  11. 2021Adversarial Attacks on Deep Models for Financial Transaction Recordscited by 24
  12. 2021Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuningcited by 24
38 more
  1. 2022Fairify: Fairness Verification of Neural Networkscited by 22
  2. 2022Pixle: a fast and effective black-box attack based on rearranging pixelscited by 20
  3. 2023Regularization properties of adversarially-trained linear regressioncited by 17
  4. 2022Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Trainingcited by 15
  5. 2021Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfittingcited by 14
  6. 2021A Person Re-identification Data Augmentation Method with Adversarial Defense Effectcited by 13
  7. 2023Adversarial Training Should Be Cast as a Non-Zero-Sum Gamecited by 13
  8. 2022Analysis and Extensions of Adversarial Training for Video Classificationcited by 11
  9. 2021Morphence: Moving Target Defense Against Adversarial Examplescited by 9
  10. 2021Perceptually Constrained Adversarial Attackscited by 7
  11. 2021Adversarial Learning with Cost-Sensitive Classescited by 6
  12. 2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
  13. 2021Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Acceleratorscited by 6
  14. 2021Towards Adversarial Patch Analysis and Certified Defense against Crowd Countingcited by 6
  15. 2023Optimization and Optimizers for Adversarial Robustnesscited by 6
  16. 2023Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropoutscited by 5
  17. 2025Adversarial Training for Defense Against Label Poisoning Attackscited by 5
  18. 2021Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptionscited by 4
  19. 2021Practical Convex Formulation of Robust One-hidden-layer Neural Network Trainingcited by 4
  20. 2022Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundariescited by 4
  21. 2023Input margins can predict generalization toocited by 4
  22. 2024AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Predictioncited by 4
  23. 2021Towards Speeding up Adversarial Training in Latent Spacescited by 3
  24. 2021Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagescited by 3
  25. 2022Learning Less Generalizable Patterns with an Asymmetrically Trained Double Classifier for Better Test-Time Adaptationcited by 3
  26. 2021Simpler Certified Radius Maximization by Propagating Covariancescited by 2
  27. 2022Memory Defense: More Robust Classification via a Memory-Masking Autoencodercited by 2
  28. 2022Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problemcited by 2
  29. 2022Adversarial Fine-tune with Dynamically Regulated Adversarycited by 2
  30. 2022Surprises in adversarially-trained linear regressioncited by 2
  31. 2022Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detectioncited by 2
  32. 2024Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Modelscited by 2
  33. 2021Improving Adversarial Robustness for Free with Snapshot Ensemblecited by 1
  34. 2021Quantifying and Understanding Adversarial Examples in Discrete Input Spacescited by 1
  35. 2021Robust Upper Bounds for Adversarial Trainingcited by 1
  36. 2022Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Trainingcited by 1
  37. 2022On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Networkscited by 1
  38. 2022Provable Robust Saliency-based Explanationscited by 1

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