mlsanityData to 7 Oct 2026

Defensive Distillation is Not Robust to Adversarial Examples

arXiv 1607.04311 ↗First posted 14 Jul 2016cs.CR cs.CVcited by 60

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Abstract (arXiv)

We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 14; 2023: 16; 2024: 5; 2025: 4; 2026: 4.

60 papers.

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  1. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 189
  2. 2023AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learningcited by 101
  3. 2021Relating Adversarially Robust Generalization to Flat Minimacited by 57
  4. 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55
  5. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 51
  6. 2021A Comprehensive Evaluation Framework for Deep Model Robustnesscited by 41
  7. 2021Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networkscited by 37
  8. 2021Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examplescited by 23
  9. 2022The Role of Machine Learning in Cybersecuritycited by 21
  10. 2022Scaling Adversarial Training to Large Perturbation Boundscited by 20
  11. 2024MirrorCheck: Efficient Adversarial Defense for Vision-Language Modelscited by 19
  12. 2021Attack to Fool and Explain Deep Networkscited by 17
38 more
  1. 2021Mutual Adversarial Training: Learning together is better than going alonecited by 17
  2. 2023Adaptive Sharpness-Aware Pruning for Robust Sparse Networkscited by 17
  3. 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
  4. 2023Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systemscited by 14
  5. 2021Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Trainingcited by 12
  6. 2022Improving Robustness by Enhancing Weak Subnetscited by 12
  7. 2025Who Taught the Lie? Responsibility Attribution for Poisoned Knowledge in Retrieval-Augmented Generationcited by 12
  8. 2021Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attackscited by 11
  9. 2024On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspectivecited by 11
  10. 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
  11. 2021Controlled Caption Generation for Images Through Adversarial Attackscited by 9
  12. 2022How many perturbations break this model? Evaluating robustness beyond adversarial accuracycited by 9
  13. 2022Transferring Adversarial Robustness Through Robust Representation Matchingcited by 7
  14. 2022Efficient Adversarial Training With Data Pruningcited by 7
  15. 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
  16. 2022TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systemscited by 7
  17. 2021Disrupting Adversarial Transferability in Deep Neural Networkscited by 6
  18. 2021Certified Federated Adversarial Trainingcited by 6
  19. 2023Revisiting adversarial training for the worst-performing classcited by 4
  20. 2021Clustering Effect of (Linearized) Adversarial Robust Modelscited by 3
  21. 2022A Simple Approach to Adversarial Robustness in Few-shot Image Classificationcited by 3
  22. 2022Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protectioncited by 3
  23. 2023Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systemscited by 3
  24. 2023Adversarial Robustness in Unsupervised Machine Learning: A Systematic Reviewcited by 3
  25. 2022An Eye for an Eye: Defending against Gradient-based Attacks with Gradientscited by 2
  26. 2023Computational Asymmetries in Robust Classificationcited by 2
  27. 2023Adversarial Attacks on Image Classification Models: Analysis and Defensecited by 2
  28. 2024PushPull-Net: Inhibition-driven ResNet robust to image corruptionscited by 2
  29. 2025GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Modelscited by 2
  30. 2025Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defensescited by 2
  31. 2021Delving into the pixels of adversarial samplescited by 1
  32. 2022Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understandingcited by 1
  33. 2023Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Modelscited by 1
  34. 2024ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNscited by 1
  35. 2021Target Training Does Adversarial Training Without Adversarial Samplescited by 0
  36. 2021Group-Structured Adversarial Trainingcited by 0
  37. 2022Decorrelative Network Architecture for Robust Electrocardiogram Classificationcited by 0
  38. 2022Symmetry Defense Against CNN Adversarial Perturbation Attackscited by 0

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