An ensemble of five pruned two-class differentiators rejects over 90% of targeted and non-targeted misclassification attacks in transfer learning with less than 10% accuracy loss, under a black-box attack model.
Making machine learning robust against adversarial inputs
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Defeating Misclassification Attacks Against Transfer Learning
An ensemble of five pruned two-class differentiators rejects over 90% of targeted and non-targeted misclassification attacks in transfer learning with less than 10% accuracy loss, under a black-box attack model.