  The deep learning algorithm has achieved great success in the field of
computer vision, but some studies have pointed out that the deep learning model
is vulnerable to attacks adversarial examples and makes false decisions. This
challenges the further development of deep learning, and urges researchers to
pay more attention to the relationship between adversarial examples attacks and
deep learning security. This work focuses on adversarial examples, optimizes
the generation of adversarial examples from the view of adversarial robustness,
takes the perturbations added in adversarial examples as the optimization
parameter. We propose RWR-NM-PGD attack algorithm based on random warm restart
mechanism and improved Nesterov momentum from the view of gradient
optimization. The algorithm introduces improved Nesterov momentum, using its
characteristics of accelerating convergence and improving gradient update
direction in optimization algorithm to accelerate the generation of adversarial
examples. In addition, the random warm restart mechanism is used for
optimization, and the projected gradient descent algorithm is used to limit the
range of the generated perturbations in each warm restart, which can obtain
better attack effect. Experiments on two public datasets show that the
algorithm proposed in this work can improve the success rate of attacking deep
learning models without extra time cost. Compared with the benchmark attack
method, the algorithm proposed in this work can achieve better attack success
rate for both normal training model and defense model. Our method has average
attack success rate of 46.3077%, which is 27.19% higher than I-FGSM and 9.27%
higher than PGD. The attack results in 13 defense models show that the attack
algorithm proposed in this work is superior to the benchmark algorithm in
attack universality and transferability.
