mlsanityData to 14 Oct 2026

Extending Defensive Distillation

arXiv 1705.05264 ↗First posted 15 May 2017cs.LG cs.CR stat.MLcited by 23

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Abstract (arXiv)

Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address its limitations. We view our results not only as an effective way of addressing some of the recently discovered attacks but also as reinforcing the importance of improved training techniques.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2017.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 5; 2024: 1; 2025: 3.

23 papers.

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  1. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66
  2. 2021AdvRush: Searching for Adversarially Robust Neural Architecturescited by 28
  3. 2022Robust Trajectory Prediction against Adversarial Attackscited by 17
  4. 2022PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognitioncited by 16
  5. 2023Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Promptingcited by 12
  6. 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
  7. 2021CARTL: Cooperative Adversarially-Robust Transfer Learningcited by 8
  8. 2024Machine Learning Robustness: A Primercited by 8
  9. 2021Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneitycited by 7
  10. 2025TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributionscited by 6
  11. 2022A Tutorial on Adversarial Learning Attacks and Countermeasurescited by 3
  12. 2025LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial Attackscited by 3
11 more
  1. 2021Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attackscited by 2
  2. 2022Can collaborative learning be private, robust and scalable?cited by 2
  3. 2022LADDER: Latent Boundary-guided Adversarial Trainingcited by 2
  4. 2023A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networkscited by 2
  5. 2021Beyond Categorical Label Representations for Image Classificationcited by 1
  6. 2023Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friendcited by 1
  7. 2021BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attackscited by 0
  8. 2021Mixing between the Cross Entropy and the Expectation Loss Termscited by 0
  9. 2022Adversarial Patterns: Building Robust Android Malware Classifierscited by 0
  10. 2023Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithmcited by 0
  11. 2025Robust Physical Adversarial Patches Using Dynamically Optimized Clusterscited by 0