  Deep Neural Network (DNN) is a widely used deep learning technique. How to
ensure the safety of DNN-based system is a critical problem for the research
and application of DNN. Robustness is an important safety property of DNN.
However, existing work of verifying DNN's robustness is time-consuming and hard
to scale to large-scale DNNs. In this paper, we propose a boosting method for
DNN robustness verification, aiming to find counter-examples earlier. Our
observation is DNN's different inputs have different possibilities of existing
counter-examples around them, and the input with a small difference between the
largest output value and the second largest output value tends to be the
achilles's heel of the DNN. We have implemented our method and applied it on
Reluplex, a state-of-the-art DNN verification tool, and four DNN attacking
methods. The results of the extensive experiments on two benchmarks indicate
the effectiveness of our boosting method.
