A Study of Robustness of Neural Nets Using Approximate Feature CollisionsDownload PDF

27 Sep 2018 (modified: 21 Dec 2018)ICLR 2019 Conference Blind SubmissionReaders: Everyone
  • Abstract: In recent years, various studies have focused on the robustness of neural nets. While it is known that neural nets are not robust to examples with adversarially chosen perturbations as a result of linear operations on the input data, we show in this paper there could be a convex polytope within which all examples are misclassified by neural nets due to the properties of ReLU activation functions. We propose a way to find such polytopes empirically and demonstrate that such polytopes exist in practice. Furthermore, we show that such polytopes exist even after constraining the examples to be a composition of image patches, resulting in perceptibly different examples at different locations in the polytope that are all misclassified.
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