Adversarial Robustness
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Preprocessing Defenses Fail on Depthwise-Separable Edge CNNs
A new preprint: six preprocessing defenses recover a residual CNN but not depthwise-separable ones, inverting the signal squeeze detectors read.
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Adversarial Training Best Practices That Survive Evaluation
A practitioner's guide to adversarial training: pick a threat model, run PGD-AT correctly, catch robust overfitting, and evaluate with AutoAttack.
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Unforeseen Attack Robustness: How the UAR Metric Works
OpenAI's Unforeseen Attack Robustness metric scores classifiers against adversarial perturbations they never trained on. What it measures and misses.
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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.