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

Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel