Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks

24 Feb 2020 Matthew J. Roos

Despite recent progress, deep neural networks generally continue to be vulnerable to so-called adversarial examples--input images with small perturbations that can result in changes in the output classifications, despite no such change in the semantic meaning to human viewers. This is true even for seemingly simple challenges such as the MNIST digit classification task... (read more)

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