  Deep Neural Networks have been widely used in many fields. However, studies
have shown that DNNs are easily attacked by adversarial examples, which have
tiny perturbations and greatly mislead the correct judgment of DNNs.
Furthermore, even if malicious attackers cannot obtain all the underlying model
parameters, they can use adversarial examples to attack various DNN-based task
systems. Researchers have proposed various defense methods to protect DNNs,
such as reducing the aggressiveness of adversarial examples by preprocessing or
improving the robustness of the model by adding modules. However, some defense
methods are only effective for small-scale examples or small perturbations but
have limited defense effects for adversarial examples with large perturbations.
This paper assigns different defense strategies to adversarial perturbations of
different strengths by grading the perturbations on the input examples.
Experimental results show that the proposed method effectively improves defense
performance. In addition, the proposed method does not modify any task model,
which can be used as a preprocessing module, which significantly reduces the
deployment cost in practical applications.
