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.

该研究提出了一种定量指标来评估神经网络模型的内在稳健性，并通过损失可视化的方式解释了对抗攻击和防御机制。该指标具有快速生成、结构和参数规模无关、可靠性高的优点，并提供了一种相对于不同测试设置不变的评估方法。