mlsanityData to 14 Oct 2026

Adversarial Attacks and Defences: A Survey

arXiv 1810.00069 ↗First posted 28 Sep 2018cs.LG cs.CR stat.MLcited by 259

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Abstract (arXiv)

Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number of tasks. As a consequence, deep learning is being extensively used in most of the recent day-to-day applications. However, security of deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify the output. In recent times, different types of adversaries based on their threat model leverage these vulnerabilities to compromise a deep learning system where adversaries have high incentives. Hence, it is extremely important to provide robustness to deep learning algorithms against these adversaries. However, there are only a few strong countermeasures which can be used in all types of attack scenarios to design a robust deep learning system. In this paper, we attempt to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.

Key facts. Cited by 259 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Sep 2018.

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Citing papers per year: 2021: 59; 2022: 55; 2023: 61; 2024: 47; 2025: 30; 2026: 7.

259 papers.

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  1. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
  2. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,095
  3. 2021Trustworthy AI: From Principles to Practicescited by 207
  4. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
  5. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  6. 2021Towards Robust and Reliable Algorithmic Recoursecited by 102
  7. 2024WAVES: Benchmarking the Robustness of Image Watermarkscited by 97
  8. 2022Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoningcited by 94
  9. 2021Trustworthy AI: A Computational Perspectivecited by 93
  10. 2024A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trendscited by 84
  11. 2023Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilitiescited by 82
  12. 2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 79
38 more
  1. 2021Black-box Detection of Backdoor Attacks with Limited Information and Datacited by 73
  2. 2025A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluationscited by 67
  3. 2021Probabilistic Margins for Instance Reweighting in Adversarial Trainingcited by 57
  4. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  5. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 52
  6. 2022Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?cited by 49
  7. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  8. 2021Reveal of Vision Transformers Robustness against Adversarial Attackscited by 43
  9. 2022Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learningcited by 43
  10. 2024A Language Model's Guide Through Latent Spacecited by 43
  11. 2022Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networkscited by 39
  12. 2024Threats, Attacks, and Defenses in Machine Unlearning: A Surveycited by 39
  13. 2021The Threat of Offensive AI to Organizationscited by 38
  14. 2021Improved Text Classification via Contrastive Adversarial Trainingcited by 38
  15. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  16. 2024Leveraging the Context through Multi-Round Interactions for Jailbreaking Attackscited by 36
  17. 2022Invariant Feature Learning for Generalized Long-Tailed Classificationcited by 34
  18. 2022DISCO: Adversarial Defense with Local Implicit Functionscited by 34
  19. 2024Understanding and Improving Training-free Loss-based Diffusion Guidancecited by 32
  20. 2021Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networkscited by 30
  21. 2024Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challengescited by 30
  22. 2021Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networkscited by 29
  23. 2021Verification of Image-based Neural Network Controllers Using Generative Modelscited by 27
  24. 2021BASAR:Black-box Attack on Skeletal Action Recognitioncited by 26
  25. 2024GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanationcited by 25
  26. 2021Adversarial Attacks on Deep Models for Financial Transaction Recordscited by 24
  27. 2023Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Modelscited by 24
  28. 2021Universal Adversarial Examples and Perturbations for Quantum Classifierscited by 23
  29. 2022A Survey of Adversarial Defences and Robustness in NLPcited by 23
  30. 2022Robust Conversational Agents against Imperceptible Toxicity Triggerscited by 23
  31. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 22
  32. 2022Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical Worldcited by 22
  33. 2022SoK: On the Semantic AI Security in Autonomous Drivingcited by 22
  34. 2023Model Leeching: An Extraction Attack Targeting LLMscited by 22
  35. 2023A Survey on the Robustness of Computer Vision Models against Common Corruptionscited by 21
  36. 2021Poisoning Attacks on Fair Machine Learningcited by 19
  37. 2022Transferable Graph Backdoor Attackcited by 19
  38. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18

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