mlsanityData to 14 Oct 2026

Training Ensembles to Detect Adversarial Examples

arXiv 1712.04006 ↗First posted 11 Dec 2017cs.LG cs.CR cs.CVcited by 10

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Abstract (arXiv)

We propose a new ensemble method for detecting and classifying adversarial examples generated by state-of-the-art attacks, including DeepFool and C&W. Our method works by training the members of an ensemble to have low classification error on random benign examples while simultaneously minimizing agreement on examples outside the training distribution. We evaluate on both MNIST and CIFAR-10, against oblivious and both white- and black-box adversaries.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2017.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 3; 2025: 1.

10 papers.

  1. 2021Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalizationcited by 83
  2. 2023Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitizationcited by 5
  3. 2024Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attackscited by 5
  4. 2024Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replaycited by 3
  5. 2021Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustnesscited by 0
  6. 2021Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instancescited by 0
  7. 2022Defending Adversarial Examples by Negative Correlation Ensemblecited by 0
  8. 2023Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Trainingcited by 0
  9. 2024Invariance-powered Trustworthy Defense via Remove Then Restorecited by 0
  10. 2025SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defensecited by 0