mlsanityData to 7 Oct 2026

Holistic Adversarial Robustness of Deep Learning Models

arXiv 2202.07201 ↗First posted 15 Feb 2022cs.LG cs.AI cs.CRcited by 11

Not on the map. Why?

Abstract (arXiv)

Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning-based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 91 of its 132 references that are in the map. First posted on arXiv 15 Feb 2022.

Builds on

91 of 132 references.

References · 91

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  6. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  7. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  8. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  9. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  10. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  11. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  12. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
38 more
  1. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  2. 2016Universal adversarial perturbationscited by 774
  3. 2018How To Backdoor Federated Learningcited by 745
  4. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 739
  5. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  6. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  7. 2017One pixel attack for fooling deep neural networkscited by 579
  8. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 564
  9. 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
  10. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  11. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 530
  12. 2017Adversarial Patchcited by 514
  13. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  14. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
  15. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  16. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  17. 2018Spectral Signatures in Backdoor Attackscited by 457
  18. 2019On Evaluating Adversarial Robustnesscited by 442
  19. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  20. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 439
  21. 2019Unlabeled Data Improves Adversarial Robustnesscited by 415
  22. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  23. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  24. 2019Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attackcited by 333
  25. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 328
  26. 2020Input-Aware Dynamic Backdoor Attackcited by 304
  27. 2017Certified Defenses for Data Poisoning Attackscited by 288
  28. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  29. 2018Towards Fast Computation of Certified Robustness for ReLU Networkscited by 254
  30. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  31. 2021Vision Transformers are Robust Learnerscited by 208
  32. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  33. 2018Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approachcited by 181
  34. 2018Certified Adversarial Robustness with Additive Noisecited by 178
  35. 2018Disentangling Adversarial Robustness and Generalizationcited by 171
  36. 2019Are Labels Required for Improving Adversarial Robustness?cited by 162
  37. 2020A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learningcited by 160
  38. 2020Witches' Brew: Industrial Scale Data Poisoning via Gradient Matchingcited by 156

50 most cited of 91 All 91 (JSON)

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 2; 2025: 2.

11 papers.

  1. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 189
  2. 2023Overload: Latency Attacks on Object Detection for Edge Devicescited by 13
  3. 2023AI Maintenance: A Robustness Perspectivecited by 6
  4. 2023Adversarial Machine Learning for Social Good: Reframing the Adversary as an Allycited by 5
  5. 2022Evaluating the Adversarial Robustness for Fourier Neural Operatorscited by 2
  6. 2023Lost In Translation: Generating Adversarial Examples Robust to Round-Trip Translationcited by 2
  7. 2024SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledgecited by 1
  8. 2024Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Costcited by 1
  9. 2022Security and Safety Aspects of AI in Industry Applicationscited by 0
  10. 2025Threat Modeling for Enhancing Security of IoT Audio Classification Devices under a Secure Protocols Frameworkcited by 0
  11. 2025Layer-wise Noise Guided Selective Wavelet Reconstruction for Robust Medical Image Segmentationcited by 0