  As deep learning applications, especially programs of computer vision, are
increasingly deployed in our lives, we have to think more urgently about the
security of these applications.One effective way to improve the security of
deep learning models is to perform adversarial training, which allows the model
to be compatible with samples that are deliberately created for use in
attacking the model.Based on this, we propose a simple architecture to build a
model with a certain degree of robustness, which improves the robustness of the
trained network by adding an adversarial sample detection network for
cooperative training. At the same time, we design a new data sampling strategy
that incorporates multiple existing attacks, allowing the model to adapt to
many different adversarial attacks with a single training.We conducted some
experiments to test the effectiveness of this design based on Cifar10 dataset,
and the results indicate that it has some degree of positive effect on the
robustness of the model.Our code could be found at
https://github.com/dowdyboy/simple_structure_for_robust_model .
