Evasion Attacks: How Adversarial Noise Bypasses ML Classifiers
The study tested adversarial attacks on image and text classifiers to show how vulnerability depends on data type. Image classification (MNIST) showed clear evasion with gradient-based attacks causing large accuracy drops. Text classification (DistilBERT) showed stability against mild, controlled text perturbations, suggesting robustness is modality-dependent.