Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks

ICLR 2019 Saeid Asgari TaghanakiShekoofeh AziziGhassan Hamarneh

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, in this paper, we propose a nonlinear radial basis convolutional feature transformation by learning the Mahalanobis distance function that maps the input convolutional features from the same class into tight clusters... (read more)

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