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Adversarial Learning with Cost-Sensitive Classes
This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples.
Incorporating Hidden Layer representation into Adversarial Attacks and Defences
In this paper, we propose a defence strategy to improve adversarial robustness by incorporating hidden layer representation.
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks
This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.
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