dl-adversarial-examples-imperceptible-perturbations

IN premise — entries/2026/06/21/wiki-Deep_learning-chunk-5.md

Created 2026-06-21T09:55:49+00:00

Adversarial examples are inputs with small, human-imperceptible perturbations that cause neural networks to confidently misclassify them.

Dependents

These beliefs depend on this one:

• IN neural-network-adversarial-vulnerability-general — Adversarial vulnerability is a general property of neural networks spanning supervised learning (imperceptible image perturbations), reinforcement learning (shared adversarial features across MDPs), and even single-pixel attacks — not a quirk of any particular architecture or domain.
• OUT neural-networks-deployment-ready — Neural networks can be reliably deployed given superhuman benchmark performance and established optimization techniques for embedded hardware.