Authors: Marco Melis,Ambra Demontis,Battista Biggio,Gavin Brown,Giorgio Fumera,Fabio Roli
ArXiv: 1708.06939
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DOI
Abstract URL: http://arxiv.org/abs/1708.06939v1
Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In this work, we aim to evaluate the extent to which
robot-vision systems embodying deep-learning algorithms are vulnerable to
adversarial examples, and propose a computationally efficient countermeasure to
mitigate this threat, based on rejecting classification of anomalous inputs. We
then provide a clearer understanding of the safety properties of deep networks
through an intuitive empirical analysis, showing that the mapping learned by
such networks essentially violates the smoothness assumption of learning
algorithms. We finally discuss the main limitations of this work, including the
creation of real-world adversarial examples, and sketch promising research
directions.