# Extending Defensive Distillation

arXiv: https://arxiv.org/abs/1705.05264 · HTML: https://mlsanity.com/paper/1705.05264/

## Key facts

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

## Built on by (most cited first, 23 of 23)

- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [AdvRush: Searching for Adversarially Robust Neural Architectures](https://mlsanity.com/paper/2108.01289/) (2021)
- [Robust Trajectory Prediction against Adversarial Attacks](https://mlsanity.com/paper/2208.00094/) (2022)
- [PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition](https://mlsanity.com/paper/2208.09801/) (2022)
- [Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting](https://mlsanity.com/paper/2308.10315/) (2023)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [CARTL: Cooperative Adversarially-Robust Transfer Learning](https://mlsanity.com/paper/2106.06667/) (2021)
- [Machine Learning Robustness: A Primer](https://mlsanity.com/paper/2404.00897/) (2024)
- [Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity](https://mlsanity.com/paper/2102.11382/) (2021)
- [TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions](https://mlsanity.com/paper/2503.01924/) (2025)
- [A Tutorial on Adversarial Learning Attacks and Countermeasures](https://mlsanity.com/paper/2202.10377/) (2022)
- [LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial Attacks](https://mlsanity.com/paper/2502.20562/) (2025)
- [Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks](https://mlsanity.com/paper/2101.02899/) (2021)
- [Can collaborative learning be private, robust and scalable?](https://mlsanity.com/paper/2205.02652/) (2022)
- [LADDER: Latent Boundary-guided Adversarial Training](https://mlsanity.com/paper/2206.03717/) (2022)
- [A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks](https://mlsanity.com/paper/2310.06468/) (2023)
- [Beyond Categorical Label Representations for Image Classification](https://mlsanity.com/paper/2104.02226/) (2021)
- [Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend](https://mlsanity.com/paper/2305.10766/) (2023)
- [BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks](https://mlsanity.com/paper/2103.08031/) (2021)
- [Mixing between the Cross Entropy and the Expectation Loss Terms](https://mlsanity.com/paper/2109.05635/) (2021)
- [Adversarial Patterns: Building Robust Android Malware Classifiers](https://mlsanity.com/paper/2203.02121/) (2022)
- [Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm](https://mlsanity.com/paper/2303.15901/) (2023)
- [Robust Physical Adversarial Patches Using Dynamically Optimized Clusters](https://mlsanity.com/paper/2511.18656/) (2025)

## 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.