# On the Effectiveness of Defensive Distillation

arXiv: https://arxiv.org/abs/1607.05113 · HTML: https://mlsanity.com/paper/1607.05113/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2016.

## Built on by (most cited first, 6 of 6)

- [Survey: Leakage and Privacy at Inference Time](https://mlsanity.com/paper/2107.01614/) (2021)
- [On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective](https://mlsanity.com/paper/2408.13809/) (2024)
- [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)
- [Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity](https://mlsanity.com/paper/2111.12906/) (2021)
- [PuriDefense: Randomized Local Implicit Adversarial Purification for Defending Black-box Query-based Attacks](https://mlsanity.com/paper/2401.10586/) (2024)
- [DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2101.03218/) (2021)

## Abstract (arXiv)

We report experimental results indicating that defensive distillation successfully mitigates adversarial samples crafted using the fast gradient sign method, in addition to those crafted using the Jacobian-based iterative attack on which the defense mechanism was originally evaluated.