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TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
[article]
2021
arXiv
pre-print
Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from different model families or training processes. To better protect ML systems against adversarial attacks, several questions are raised: what are the sufficient conditions for adversarial transferability and how to bound it? Is there a way to reduce the adversarial transferability in order to improve the robustness of an
arXiv:2104.00671v2
fatcat:qaea2rjyefdrthmfv3dyv5przy