mlsanityData to 14 Oct 2026

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

arXiv 1610.00768 ↗First posted 3 Oct 2016cs.LG cs.CR stat.MLcited by 124

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Abstract (arXiv)

CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be used to develop more robust machine learning models and to provide standardized benchmarks of models' performance in the adversarial setting. Benchmarks constructed without a standardized implementation of adversarial example construction are not comparable to each other, because a good result may indicate a robust model or it may merely indicate a weak implementation of the adversarial example construction procedure. This technical report is structured as follows. Section 1 provides an overview of adversarial examples in machine learning and of the CleverHans software. Section 2 presents the core functionalities of the library: namely the attacks based on adversarial examples and defenses to improve the robustness of machine learning models to these attacks. Section 3 describes how to report benchmark results using the library. Section 4 describes the versioning system.

Key facts. Cited by 124 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Oct 2016.

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Citing papers per year: 2021: 36; 2022: 34; 2023: 20; 2024: 16; 2025: 13; 2026: 5.

124 papers.

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  1. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
  2. 2021ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Modelscited by 85
  3. 2023A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinkingcited by 85
  4. 2021Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Imagescited by 58
  5. 2021Person Re-identification Method Based on Color Attack and Joint Defencecited by 56
  6. 2022CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Modelscited by 53
  7. 2022Assaying Out-Of-Distribution Generalization in Transfer Learningcited by 53
  8. 2023Fast Decision Boundary based Out-of-Distribution Detectorcited by 49
  9. 2021Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Datacited by 45
  10. 2021The Threat of Offensive AI to Organizationscited by 38
  11. 2022LoMar: A Local Defense Against Poisoning Attack on Federated Learningcited by 31
  12. 2021FoveaTer: Foveated Transformer for Image Classificationcited by 28
38 more
  1. 2023Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanationscited by 27
  2. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 20
  3. 2022Efficiently Training Low-Curvature Neural Networkscited by 19
  4. 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
  5. 2025LLM-Safety Evaluations Lack Robustnesscited by 18
  6. 2022Can we achieve robustness from data alone?cited by 16
  7. 2024AttackBench: Evaluating Gradient-based Attacks for Adversarial Examplescited by 14
  8. 2021Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretationcited by 12
  9. 2022Topology and geometry of data manifold in deep learningcited by 12
  10. 2022How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?cited by 11
  11. 2021Detecting AutoAttack Perturbations in the Frequency Domaincited by 10
  12. 2021Launching Adversarial Attacks against Network Intrusion Detection Systems for IoTcited by 9
  13. 2022Tools and Practices for Responsible AI Engineeringcited by 9
  14. 2021Evaluating Document Coherence Modellingcited by 8
  15. 2021PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attackcited by 8
  16. 2021Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoffcited by 8
  17. 2021Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysiscited by 7
  18. 2021Perceptually Constrained Adversarial Attackscited by 7
  19. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  20. 2022A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challengescited by 7
  21. 2022Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Featurescited by 7
  22. 2022SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usabilitycited by 7
  23. 2024Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysiscited by 7
  24. 2021Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systemscited by 6
  25. 2023Optimization and Optimizers for Adversarial Robustnesscited by 6
  26. 2023Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairlycited by 6
  27. 2025AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Researchcited by 6
  28. 2021On managing vulnerabilities in AI/ML systemscited by 5
  29. 2021Adversarial Information Bottleneckcited by 5
  30. 2022Subverting Fair Image Search with Generative Adversarial Perturbationscited by 5
  31. 2022Dynamic Time Warping based Adversarial Framework for Time-Series Domaincited by 5
  32. 2022Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerabilitycited by 5
  33. 2021Robust Vision-Based Cheat Detection in Competitive Gamingcited by 4
  34. 2021Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptionscited by 4
  35. 2022On Certifying and Improving Generalization to Unseen Domainscited by 4
  36. 2023SecurityNet: Assessing Machine Learning Vulnerabilities on Public Modelscited by 4
  37. 2021ExAD: An Ensemble Approach for Explanation-based Adversarial Detectioncited by 3
  38. 2021Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectorscited by 3

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