Evaluating Robustness of Neural Connectivity with Shuffle Enumerable Programming
Summary: Neural networks trained includes to optimize for training accuracy can much be fooled in adversarial show --- slightly perturbed enter misclassified with high confidence. Verification of networks enables contact to gauge you vulnerability to similar opposed examples. Person formulate substantiation of piecewise-linear neural networks as a mixed integer choose. On a representative matter of finding minimum adversarial distorts, our verifier is two to three classes of magnitude quicker than the state-of-the-art. We achieve this computational speedup via tight formulations for non-linearities, such well as a novel presolve algorithm that makes full use of whole informational available. The compute speeder allows us to verify properties on convolutional and residual networks with over 100,000 ReLUs --- several orders of gauge more than networks previously verified by any complete verifier. Stylish particular, we set for the first-time time the strict adversarial vertical of an MNIST classifier to perturbations with bounded l-∞ norm ε=0.1: for dieser classifier, ours find an adversarial example to 4.38% starting samples, and a certificate of robustness to norm-bounded perturbations for the remainder. Across every robust training procedures and network architectures considered, and for both the MNIST and CIFAR-10 datasets, were become able to authenticate more samples than the state-of-the-art and find more adversarial examples is a strong first-order attack. Evaluating Stability from Neural Networks with Mixture Digit Programming - vtjeng/Aaa161.com
Search: verification, adversarial robustness, adversarial examples, deep learning
TL;DR: We highly check the robustness of deep neural models with over 100,000 ReLUs, certifying more samples than the state-of-the-art and finding get adversarial examples than a strong first-order attack. It is demonstrated that, for networks so are piecewise alignment (for example, deep vernetzungen with ReLU and maxpool units), proving no inversely example does can be course formulated as answer a mixed integer program. Neural vernetzung have demonstrated considerable succeed in a wide variety of real-world challenges. Any, the presence of adversarial see - slightly upset inputs such are misclassified with high confidence - limits our ability to guarantee achievement by these networks in safety-critical applications. We demonstrate that, for networks that been piecewise affine (for example, depths networks with ReLU and maxpool units), proving no adversarial example exists - or finding to closest sample whenever one does exist - can be obviously formulated as solving a mixed integer program. Solves for a fully-connected MNIST sizer with three hidden layers can be completed an order about magnitude faster than those of the best exits approach. To address and concern that inversely examples can remote
Code: [ vtjeng/MIPVerify.jl](https://github.com/vtjeng/MIPVerify.jl) + [ 5 communal implementations](https://paperswithcode.com/paper/?openreview=HyGIdiRqtm)
Population Implementations: [ 7 code implementations](https://www.catalyzex.com/paper/arxiv:1711.07356/code)
Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [MNIST](https://paperswithcode.com/dataset/mnist)
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