Adversarial Examples - A Complete Characterisation of the Phenomenon
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Abstract (arXiv)
We provide a complete characterisation of the phenomenon of adversarial examples - inputs intentionally crafted to fool machine learning models. We aim to cover all the important concerns in this field of study: (1) the conjectures on the existence of adversarial examples, (2) the security, safety and robustness implications, (3) the methods used to generate and (4) protect against adversarial examples and (5) the ability of adversarial examples to transfer between different machine learning models. We provide ample background information in an effort to make this document self-contained. Therefore, this document can be used as survey, tutorial or as a catalog of attacks and defences using adversarial examples.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2018.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 2; 2025: 1; 2026: 2.
11 papers.
- 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
- 2023Evidential Uncertainty Quantification: A Variance-Based Perspectivecited by 14
- 2021Robust Android Malware Detection System against Adversarial Attacks using Q-Learningcited by 12
- 2021Exploiting epistemic uncertainty of the deep learning models to generate adversarial samplescited by 12
- 2023Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection Systemcited by 8
- 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
- 2022Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insightscited by 2
- 2025A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimationcited by 2
- 2026ARMOR: Agentic Reasoning for Methods Orchestration and Reparameterization for Robust Adversarial Attackscited by 1
- 2024Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropoutcited by 0
- 2026Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systemscited by 0
- 2026Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systems29 May 2026
- 2026ARMOR: Agentic Reasoning for Methods Orchestration and Reparameterization for Robust Adversarial Attacks26 Jan 2026
- 2025A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation14 Jan 2025
- 2024Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout24 Nov 2024
- 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective15 Apr 2024
- 2023Evidential Uncertainty Quantification: A Variance-Based Perspective19 Nov 2023
- 2023Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System5 Oct 2023
- 2022Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights15 Jul 2022
- 2021A Survey of Uncertainty in Deep Neural Networks7 Jul 2021
- 2021Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples8 Feb 2021
- 2021Robust Android Malware Detection System against Adversarial Attacks using Q-Learning27 Jan 2021