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Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers
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. Under FGSM, accuracy fell to 60.20% at $ε$ = 0.
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