Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers

Reported by arXiv cs.CR (Parker Hummel (Minot State University), Ryne Skabo (Minot State University), Muhammad Abusaqer (Minot State University))

Summary by Cyber Security News2 Oct 2026, 06:00 CEST

About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.00136

arXiv:2610.00136v1 Announce Type: new Abstract: This paper presents a reproducible, educational study of evasion attacks in image classification and text classification. A compact convolutional network trained on MNIST reached 98.63% clean test accuracy and was evaluated under two white-box attacks.

Read the full article at arXiv cs.CR →

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