Adversarial Defense via Learning to Generate Diverse Attacks

ICCV 2019 Yunseok Jang Tianchen Zhao Seunghoon Hong Honglak Lee

With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been applied as dominant tools to various machine learning domains. Despite this success, however, it has been found that DNNs are surprisingly vulnerable to malicious attacks; adding a small, perceptually indistinguishable perturbations to the data can easily degrade classification performance... (read more)

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