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With friends like these, who needs adversaries?

Abstract:

The vulnerability of deep image classification networks to adversarial attack is now well known, but less well understood. Via a novel experimental analysis, we illustrate some facts about deep convolutional networks for image classification that shed new light on their behaviour and how it connects to the problem of adversaries. In short, the celebrated performance of these networks and their vulnerability to adversarial attack are simply two sides of the same coin: the input image-space dir...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
Neural Information Processing Systems Proceedings Publisher's website
Journal:
Neural Information Processing Systems 2018 Journal website
Host title:
32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada
Publication date:
2018-12-20
Acceptance date:
2018-09-05
Source identifiers:
953446
Pubs id:
pubs:953446
UUID:
uuid:bbc48c6f-d259-40a5-967a-106978f3d1d9
Local pid:
pubs:953446
Deposit date:
2018-12-19

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