Image Classifiers
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How Adversarial Examples Fool Image Classifiers
Adversarial examples follow the model's own gradient: a 0.007 per-pixel nudge made GoogLeNet call a panda a gibbon at 99.3%. How and why it works.
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Universal Adversarial Perturbations: One Vector, Many Inputs
Universal adversarial perturbations are input-agnostic: one crafted noise vector causes misclassification across most inputs and transfers between models.
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Evasion Attacks on Image Classifiers: FGSM, PGD, and C&W
A technical comparison of FGSM, PGD, and C&W explains their optimization goals, threat models, benchmark roles, limitations, and defensive implications.