# Adversarial Machine Learning
Status: public
Confidence: medium (0.815) (verified)
Last verified: 2026-05-30
Generation: ai_structured


## TL;DR

Adversarial machine learning studies inputs, training data, and model interactions that are intentionally crafted to make machine-learning systems fail. This entry focuses on the foundational adversarial-example line of work: small input perturbations, gradient-based attacks, and adversarial training.

## Core Claims

Early adversarial-example work showed that neural networks can make confident mistakes on inputs changed by small, targeted perturbations. That result made robustness a core concern for safety-critical machine-learning systems.

The fast gradient sign method is a simple gradient-based attack. It perturbs an input in the direction indicated by the sign of the loss gradient, making it a compact baseline for studying adversarial examples.

Madry et al. framed robustness as an optimization problem against adversarial perturbations and used projected gradient descent adversaries in training. This helped establish adversarial training as a central empirical defense baseline.

## Citation Boundaries

Use this article for foundational adversarial-example concepts. Do not use it for current threat intelligence, current defense performance rankings, or claims that any single defense fully solves adversarial robustness.

## Further Reading

- [Intriguing Properties of Neural Networks](https://arxiv.org/abs/1312.6199)
- [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083)