#adversarial-examples
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Adversarial Attack Libraries: ART, Foolbox, torchattacks
ART, Foolbox, torchattacks, CleverHans and TextAttack compared on scope, framework support, attack coverage, licence and repository maintenance.
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Adversarial Examples vs. Data Poisoning: Timing Is Everything
Adversarial examples attack a deployed model at inference; data poisoning attacks the model before it is deployed. Different timing, different defenses.
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Adversarial Patch Attacks: Physical Perturbations That Fool ML
Adversarial patches are large, visible, localized perturbations designed to survive physical-world conditions — printing, lighting, and camera optics.
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Universal Adversarial Perturbations: One Vector That Fools 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
The three foundational gradient-based evasion attacks, what each one actually optimizes, and what the benchmark numbers mean when you're evaluating a defense.
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Adversarial Transferability: Why Black-Box Attacks Work at All
Adversarial examples transfer across models with different architectures and training sets. Why that happens, and what it means for black-box defenses.