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arXiv · 1903.12519

A Provable Defense for Deep Residual Networks

Matthew Mirman·Gagandeep Singh·Martin Vechev
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Abstract

We present a training system, which can provably defend significantly larger neural networks than previously possible, including ResNet-34 and DenseNet-100. Our approach is based on differentiable abstract interpretation and introduces two novel concepts: (i) abstract layers for fine-tuning the precision and scalability of the abstraction, (ii) a flexible domain specific language (DSL) for describing training objectives that combine abstract and concrete losses with arbitrary specifications. Our training method is implemented in the DiffAI system.

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BibTeXRIS

Matthew Mirman, Gagandeep Singh, Martin Vechev. 2019-03-29. A Provable Defense for Deep Residual Networks. https://arxiv.org/abs/1903.12519

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