A neural rejection system with an SVM and a rejection threshold defends universal adversarial perturbations in radio modulation classification with about 20% higher accuracy than an undefended DNN.
Imagenet classification with deep convolutional neural networks,
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A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification
A neural rejection system with an SVM and a rejection threshold defends universal adversarial perturbations in radio modulation classification with about 20% higher accuracy than an undefended DNN.