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Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics
In this paper, we present two metrics which are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in multiclass classification tasks.
Heat and Blur: An Effective and Fast Defense Against Adversarial Examples
The growing incorporation of artificial neural networks (NNs) into many fields, and especially into life-critical systems, is restrained by their vulnerability to adversarial examples (AEs).
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