  Black-box attacks usually face two problems: poor transferability and the
inability to evade the adversarial defense. To overcome these shortcomings, we
create an original approach to generate adversarial examples by smoothing the
linear structure of the texture in the benign image, called AdvSmo. We
construct the adversarial examples without relying on any internal information
to the target model and design the imperceptible-high attack success rate
constraint to guide the Gabor filter to select appropriate angles and scales to
smooth the linear texture from the input images to generate adversarial
examples. Benefiting from the above design concept, AdvSmo will generate
adversarial examples with strong transferability and solid evasiveness.
Finally, compared to the four advanced black-box adversarial attack methods,
for the eight target models, the results show that AdvSmo improves the average
attack success rate by 9% on the CIFAR-10 and 16% on the Tiny-ImageNet dataset
compared to the best of these attack methods.
