mlsanityData to 7 Oct 2026

Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications

arXiv 2108.10451 ↗First posted 24 Aug 2021cs.LG cs.AIcited by 5

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Abstract (arXiv)

This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning, presenting a well-structured review of up-to-date techniques to assess the vulnerability of various types of deep learning models to adversarial examples. This tutorial will particularly highlight state-of-the-art techniques in adversarial attacks and robustness verification of deep neural networks (DNNs). We will also introduce some effective countermeasures to improve the robustness of deep learning models, with a particular focus on adversarial training. We aim to provide a comprehensive overall picture about this emerging direction and enable the community to be aware of the urgency and importance of designing robust deep learning models in safety-critical data analytical applications, ultimately enabling the end-users to trust deep learning classifiers. We will also summarize potential research directions concerning the adversarial robustness of deep learning, and its potential benefits to enable accountable and trustworthy deep learning-based data analytical systems and applications.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 39 of its 49 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

39 of 49 references.

References · 39

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  6. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  7. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  8. 2016Adversarial Machine Learning at Scalecited by 934
  9. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 918
  10. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 739
  11. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 663
  12. 2018Lipschitz regularity of deep neural networks: analysis and efficient estimationcited by 413
27 more
  1. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
  2. 2018Adversarially Robust Generalization Requires More Datacited by 318
  3. 2017DeepXplore: Automated Whitebox Testing of Deep Learning Systemscited by 307
  4. 2016Safety Verification of Deep Neural Networkscited by 262
  5. 2018Spatially Transformed Adversarial Examplescited by 195
  6. 2018AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networkscited by 137
  7. 2018Rademacher Complexity for Adversarially Robust Generalizationcited by 132
  8. 2019Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Trainingcited by 95
  9. 2018Reachability Analysis of Deep Neural Networks with Provable Guaranteescited by 93
  10. 2019AdvPC: Transferable Adversarial Perturbations on 3D Point Cloudscited by 84
  11. 2017Output Reachable Set Estimation and Verification for Multi-Layer Neural Networkscited by 79
  12. 2018Concolic Testing for Deep Neural Networkscited by 51
  13. 2017Feature-Guided Black-Box Safety Testing of Deep Neural Networkscited by 48
  14. 2019Practical Hidden Voice Attacks against Speech and Speaker Recognition Systemscited by 34
  15. 2018A Game-Based Approximate Verification of Deep Neural Networks with Provable Guaranteescited by 33
  16. 2020How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networkscited by 32
  17. 2019Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verificationcited by 28
  18. 2020A Safety Framework for Critical Systems Utilising Deep Neural Networkscited by 19
  19. 2021Adversarial Driving: Attacking End-to-End Autonomous Drivingcited by 19
  20. 2020Generalizing Universal Adversarial Attacks Beyond Additive Perturbationscited by 17
  21. 2019Coverage Guided Testing for Recurrent Neural Networkscited by 15
  22. 2021Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profilescited by 11
  23. 2021Improving Model Robustness with Latent Distribution Locally and Globallycited by 9
  24. 2020Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Taskcited by 6
  25. 2021Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Maskscited by 5
  26. 2020Towards the Quantification of Safety Risks in Deep Neural Networkscited by 4
  27. 2021Tutorials on Testing Neural Networkscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

5 papers.

  1. 2021Sparse Adversarial Video Attacks with Spatial Transformationscited by 11
  2. 2023Towards Verifying the Geometric Robustness of Large-scale Neural Networkscited by 11
  3. 2023Reachability Analysis of Neural Network Control Systemscited by 8
  4. 2022AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradientcited by 0
  5. 2026Robustness, Cost, and Attack-Surface Concentration in Phishing Detectioncited by 0