mlsanityData to 7 Oct 2026

Adversarial Examples: Opportunities and Challenges

arXiv 1809.04790 ↗First posted 13 Sep 2018cs.LG stat.MLcited by 62

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Abstract (arXiv)

Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from real examples, AEs can mislead the model to predict incorrect outputs while hardly be distinguished by human eyes, therefore threaten security-critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of artificial intelligence (AI) security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristics and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that, we review the existing defenses and discuss their limitations. Finally, future research opportunities and challenges on AEs are prospected.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2018.

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Citing papers per year: 2021: 14; 2022: 11; 2023: 14; 2024: 13; 2025: 8; 2026: 2.

62 papers.

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  1. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  2. 2024A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trendscited by 82
  3. 2023A Survey on Transferability of Adversarial Examples across Deep Neural Networkscited by 43
  4. 2023Among Us: Adversarially Robust Collaborative Perception by Consensuscited by 37
  5. 2022On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Drivingcited by 30
  6. 2022Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systemscited by 27
  7. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  8. 2021Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Appscited by 19
  9. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 19
  10. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  11. 2021Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Votingcited by 16
  12. 2021Security and Privacy for Artificial Intelligence: Opportunities and Challengescited by 16
38 more
  1. 2023Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systemscited by 14
  2. 2022On the Robustness of Bayesian Neural Networks to Adversarial Attackscited by 12
  3. 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
  4. 2021Demystifying the Transferability of Adversarial Attacks in Computer Networkscited by 9
  5. 2023SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detectioncited by 9
  6. 2023In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Searchcited by 9
  7. 2022CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Modelscited by 8
  8. 2023State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systemscited by 8
  9. 2021Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunitiescited by 7
  10. 2024U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AIcited by 7
  11. 2022Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learningcited by 6
  12. 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systemscited by 6
  13. 2024Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structurecited by 6
  14. 2024Understanding Model Ensemble in Transferable Adversarial Attackcited by 6
  15. 2022Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomizationcited by 5
  16. 2023Towards Robust and Accurate Visual Promptingcited by 5
  17. 2025Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systemscited by 4
  18. 2021Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimizationcited by 3
  19. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  20. 2023Adversary Aware Continual Learningcited by 3
  21. 2024Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolutioncited by 3
  22. 2021Adversarial Examples Detection with Bayesian Neural Networkcited by 2
  23. 2022Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attackscited by 2
  24. 2024CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domaincited by 2
  25. 2024Edge Detectors Can Make Deep Convolutional Neural Networks More Robustcited by 2
  26. 2021PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attackcited by 1
  27. 2021STARdom: an architecture for trusted and secure human-centered manufacturing systemscited by 1
  28. 2023The Pros and Cons of Adversarial Robustnesscited by 1
  29. 2024Cloud-based XAI Services for Assessing Open Repository Models Under Adversarial Attackscited by 1
  30. 2025CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systemscited by 1
  31. 2025Wormhole Dynamics in Deep Neural Networkscited by 1
  32. 2025A Versatile Framework for Designing Group-Sparse Adversarial Attackscited by 1
  33. 2026Hierarchically Robust Zero-shot Vision-language Modelscited by 1
  34. 2021Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attackscited by 0
  35. 2021Enhanced countering adversarial attacks via input denoising and feature restoringcited by 0
  36. 2021Using a GAN to Generate Adversarial Examples to Facial Image Recognitioncited by 0
  37. 2022Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Trainingcited by 0
  38. 2022Deep Fake Detection, Deterrence and Response: Challenges and Opportunitiescited by 0

50 most cited of 62 All 62 (JSON)