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Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense
[article]
2020
arXiv
pre-print
Deep neural networks are learning models having achieved state of the art performance in many fields like prediction, computer vision, language processing and so on. However, it has been shown that certain inputs exist which would not trick a human normally, but may mislead the model completely. These inputs are known as adversarial inputs. These inputs pose a high security threat when such models are used in real world applications. In this work, we have analyzed the resistance of three
arXiv:2006.01408v1
fatcat:einvwso3tjhkfjvyou2vnbfv7y