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Poster
Certifying Geometric Robustness of Neural Networks
Mislav Balunovic · Maximilian Baader · Gagandeep Singh · Timon Gehr · Martin Vechev

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #13
in Algorithms -- Adversarial Learning »

The use of neural networks in safety-critical computer vision systems calls for their robustness certification against natural geometric transformations (e.g., rotation, scaling). However, current certification methods target mostly norm-based pixel perturbations and cannot certify robustness against geometric transformations. In this work, we propose a new method to compute sound and asymptotically optimal linear relaxations for any composition of transformations. Our method is based on a novel combination of sampling and optimization. We implemented the method in a system called DeepG and demonstrated that it certifies significantly more complex geometric transformations than existing methods on both defended and undefended networks while scaling to large architectures.

Keywords: Privacy, Anonymity, and Security · Applications · Algorithms · Adversarial Learning
[ Paper]  [ Poster] 

Author Information

Mislav Balunovic (ETH Zurich)
Maximilian Baader (ETH Zürich)
Gagandeep Singh (ETH Zurich)
Timon Gehr (ETH Zurich)
Martin Vechev (ETH Zurich, Switzerland)

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