  Recently, adversarial deception becomes one of the most considerable threats
to deep neural networks. However, compared to extensive research in new designs
of various adversarial attacks and defenses, the neural networks' intrinsic
robustness property is still lack of thorough investigation. This work aims to
qualitatively interpret the adversarial attack and defense mechanism through
loss visualization, and establish a quantitative metric to evaluate the neural
network model's intrinsic robustness. The proposed robustness metric identifies
the upper bound of a model's prediction divergence in the given domain and thus
indicates whether the model can maintain a stable prediction. With extensive
experiments, our metric demonstrates several advantages over conventional
adversarial testing accuracy based robustness estimation: (1) it provides a
uniformed evaluation to models with different structures and parameter scales;
(2) it over-performs conventional accuracy based robustness estimation and
provides a more reliable evaluation that is invariant to different test
settings; (3) it can be fast generated without considerable testing cost.
