The adversarial robustness of deep neural networks is of great significance in the field of image recognition. Relevant studies focus on the generation of adversarial samples and the robustness enhancement of defense models but lack comprehensive and objective evaluation. Thus, an effective benchmark to evaluate the adversarial robustness of image classification tasks is developed. The main functions of this system are list display, adversarial algorithm evaluation, and system optimization management. At the same time, computing resource scheduling and container scheduling are applied to ensure the evaluation task. This system can not only provide a dynamic import interface for a variety of attack and defense algorithms but also evaluate the advantages and disadvantages of the existing algorithms from all aspects in the confrontation between attack and defense algorithms.