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Reducing Adversarial Example Transferability Using Gradient Regularization
[article]
2019
arXiv
pre-print
Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples transfer between different architectures trained on different datasets. We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models. To demonstrate the relevance of this approach, we perform case studies that involve jointly training
arXiv:1904.07980v1
fatcat:ezgdgaaerjhmpghzgbbymd4dou