mlsanityData to 7 Oct 2026

MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples

arXiv 1711.08478 ↗First posted 22 Nov 2017cs.LG cs.AI cs.CRcited by 46

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Abstract (arXiv)

MagNet and "Efficient Defenses..." were recently proposed as a defense to adversarial examples. We find that we can construct adversarial examples that defeat these defenses with only a slight increase in distortion.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Nov 2017.

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Citing papers per year: 2021: 20; 2022: 7; 2023: 10; 2024: 3; 2025: 4; 2026: 2.

46 papers.

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  1. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  2. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 51
  3. 2021Physical Adversarial Attacks on an Aerial Imagery Object Detectorcited by 35
  4. 2021Towards Defending against Adversarial Examples via Attack-Invariant Featurescited by 25
  5. 2021Removing Adversarial Noise in Class Activation Feature Spacecited by 18
  6. 2021Attack to Fool and Explain Deep Networkscited by 17
  7. 2021SoK: Anti-Facial Recognition Technologycited by 16
  8. 2021Attack as Defense: Characterizing Adversarial Examples using Robustnesscited by 14
  9. 2022Improving Robustness by Enhancing Weak Subnetscited by 12
  10. 2022What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradictioncited by 10
  11. 2021Increasing the Confidence of Deep Neural Networks by Coverage Analysiscited by 9
  12. 2021Revisiting Model's Uncertainty and Confidences for Adversarial Example Detectioncited by 9
34 more
  1. 2021Modeling Adversarial Noise for Adversarial Trainingcited by 8
  2. 2023Learning Provably Robust Estimators for Inverse Problems via Jitteringcited by 8
  3. 2021Self-Supervised Adversarial Example Detection by Disentangled Representationcited by 7
  4. 2025Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Trackcited by 7
  5. 2021Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networkscited by 6
  6. 2022Deep-Attack over the Deep Reinforcement Learningcited by 5
  7. 2022Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detectioncited by 5
  8. 2023Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filterscited by 4
  9. 2024A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunitiescited by 4
  10. 2025Position: Certified Robustness Does Not (Yet) Imply Model Securitycited by 4
  11. 2021Online Adversarial Attackscited by 3
  12. 2021Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Trainingcited by 3
  13. 2022Adversarial Robustness in Deep Learning: Attacks on Fragile Neuronscited by 3
  14. 2023Individual Fairness in Bayesian Neural Networkscited by 3
  15. 2021"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Modelscited by 2
  16. 2023Computational Asymmetries in Robust Classificationcited by 2
  17. 2021Improving Global Adversarial Robustness Generalization With Adversarially Trained GANcited by 1
  18. 2021Delving into the pixels of adversarial samplescited by 1
  19. 2023Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Modelscited by 1
  20. 2023A reading survey on adversarial machine learning: Adversarial attacks and their understandingcited by 1
  21. 2023Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecastingcited by 1
  22. 2024Classification-Denoising Networkscited by 1
  23. 2021Target Training Does Adversarial Training Without Adversarial Samplescited by 0
  24. 2021Adversarial examples attack based on random warm restart mechanism and improved Nesterov momentumcited by 0
  25. 2022A Perturbation Resistant Transformation and Classification System for Deep Neural Networkscited by 0
  26. 2022Symmetry Defense Against CNN Adversarial Perturbation Attackscited by 0
  27. 2023A Minimax Approach Against Multi-Armed Adversarial Attacks Detectioncited by 0
  28. 2023Symmetry Defense Against XGBoost Adversarial Perturbation Attackscited by 0
  29. 2023Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learningcited by 0
  30. 2024Optimal Zero-Shot Detector for Multi-Armed Attackscited by 0
  31. 2025Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustnesscited by 0
  32. 2025Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecastingcited by 0
  33. 2026Statistical Inference for Adversarial Training: Central Limit Theorems via Optimal Transportcited by 0
  34. 2026Adversarially Robust PAC Learning with Optimal VC Ratescited by 0