mlsanityData to 7 Oct 2026

Adversarial Examples on Object Recognition: A Comprehensive Survey

arXiv 2008.04094 ↗First posted 7 Aug 2020cs.CV cs.LGcited by 23

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Abstract (arXiv)

Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small perturbations of inputs can be sufficient to induce incorrect behavior. Such perturbations, called adversarial examples, are intentionally designed to test the network's sensitivity to distribution drifts. Given their surprisingly small size, a wide body of literature conjectures on their existence and how this phenomenon can be mitigated. In this article we discuss the impact of adversarial examples on security, safety, and robustness of neural networks. We start by introducing the hypotheses behind their existence, the methods used to construct or protect against them, and the capacity to transfer adversarial examples between different machine learning models. Altogether, the goal is to provide a comprehensive and self-contained survey of this growing field of research.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2020.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 3; 2024: 4; 2025: 3.

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  1. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  2. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 51
  3. 2021A Review of Adversarial Attack and Defense for Classification Methodscited by 29
  4. 2023A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attackscited by 19
  5. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  6. 2022Visual Adversarial Attacks and Defenses in the Physical World: A Surveycited by 11
  7. 2021Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspectivecited by 9
  8. 2023It Is All About Data: A Survey on the Effects of Data on Adversarial Robustnesscited by 9
  9. 2022On the Robustness of Explanations of Deep Neural Network Models: A Surveycited by 5
  10. 2025Energy-Latency Attacks: A New Adversarial Threat to Deep Learningcited by 5
  11. 2025Monitoring Robustness and Individual Fairnesscited by 3
  12. 2022A causal model of safety assurance for machine learningcited by 2
11 more
  1. 2024A Survey of Neural Network Robustness Assessment in Image Recognitioncited by 2
  2. 2025An Investigation of Visual Foundation Models Robustnesscited by 2
  3. 2021Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integrationcited by 1
  4. 2022Guiding the retraining of convolutional neural networks against adversarial inputscited by 1
  5. 2023Towards the Transferable Audio Adversarial Attack via Ensemble Methodscited by 1
  6. 2024Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATRcited by 1
  7. 2024Investigating the Impact of Hard Samples on Accuracy Reveals In-class Data Imbalancecited by 1
  8. 2022A Domain-Theoretic Framework for Robustness Analysis of Neural Networkscited by 0
  9. 2022Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detectioncited by 0
  10. 2022Adversarial Detection by Approximation of Ensemble Boundarycited by 0
  11. 2024PEAS: A Strategy for Crafting Transferable Adversarial Examplescited by 0