Abstract
Despite impressive capabilities and outstanding performance, deep neural network(DNN) has captured increasing public concern for its security problem, due to frequent occurrence of erroneous behaviors. Therefore, it is necessary to conduct systematically testing before its deployment to real-world applications. Existing testing methods have provided fine-grained criteria based on neuron coverage and reached high exploratory degree of testing. But there is still a gap between the neuron coverage and model's robustness evaluation. To bridge the gap, we observed that neurons which change the activation value dramatically due to minor perturbation are prone to trigger incorrect corner cases. Motivated by it, we propose neuron sensitivity and develop a novel white-box testing framework for DNN, donated as DeepSensor. The number of sensitive neurons is maximized by particle swarm optimization, thus diverse corner cases could be triggered and neuron coverage be further improved when compared with baselines. Besides, considerable robustness enhancement can be reached when adopting testing examples based on neuron sensitivity for retraining. Extensive experiments implemented on scalable datasets and models can well demonstrate the testing effectiveness and robustness improvement of DeepSensor.
Abstract (translated)
URL
https://arxiv.org/abs/2202.07464
