mlsanityData to 14 Oct 2026

Robustness to Adversarial Examples through an Ensemble of Specialists

arXiv 1702.06856 ↗First posted 22 Feb 2017cs.NE cs.LGcited by 21

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Abstract (arXiv)

We are proposing to use an ensemble of diverse specialists, where speciality is defined according to the confusion matrix. Indeed, we observed that for adversarial instances originating from a given class, labeling tend to be done into a small subset of (incorrect) classes. Therefore, we argue that an ensemble of specialists should be better able to identify and reject fooling instances, with a high entropy (i.e., disagreement) over the decisions in the presence of adversaries. Experimental results obtained confirm that interpretation, opening a way to make the system more robust to adversarial examples through a rejection mechanism, rather than trying to classify them properly at any cost.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Feb 2017.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 3; 2024: 3; 2025: 1.

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  1. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  2. 2021Security and Privacy for Artificial Intelligence: Opportunities and Challengescited by 16
  3. 2021DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classificationcited by 10
  4. 2021Adversarial Learning with Cost-Sensitive Classescited by 6
  5. 2022Learning Sample Reweighting for Accuracy and Adversarial Robustnesscited by 6
  6. 2024Uncertainty-Aware Deep Video Compression with Ensemblescited by 5
  7. 2024Towards Adversarial Robustness of Model-Level Mixture-of-Experts Architectures for Semantic Segmentationcited by 5
  8. 2022Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINOcited by 4
  9. 2021Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approachescited by 3
  10. 2023Adversarial Robustness in Unsupervised Machine Learning: A Systematic Reviewcited by 3
  11. 2021Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustnesscited by 2
  12. 2022On the reversibility of adversarial attackscited by 1
9 more
  1. 2022Ares: A System-Oriented Wargame Framework for Adversarial MLcited by 1
  2. 2023Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codescited by 1
  3. 2024CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applicationscited by 1
  4. 2021Code Integrity Attestation for PLCs using Black Box Neural Network Predictionscited by 0
  5. 2021Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learningcited by 0
  6. 2021Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attackscited by 0
  7. 2022Defending Adversarial Examples by Negative Correlation Ensemblecited by 0
  8. 2022A Perturbation Resistant Transformation and Classification System for Deep Neural Networkscited by 0
  9. 2023Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Trainingcited by 0