ESTIMATING ROBUSTNESS OF DEEP LEARNING MODELS BY THREE ADVERSARIAL ATTACKS

Abstract

Deep learning is currently an area of ​​interest in research and development by scientists around the world. Deep learning models are deployed and applied in practice for work and social life. However, deep learning has many potential risks related to security in applications, especially recently adversarial attacks using adversarial examples are a big challenge for deep learning in particular and machine learning in general. To test the robustness of the machine learning model, we propose to use three adversarial attacks to calculate the benchmark, the experimental attack methods on the MS-COCO dataset are being used to train the machine learning model, training and testing for the YOLO model. The article summarizes the results of the successful attack rate using the proposed indicators according to the research through the experimental process conducted by the authors to verify the robustness of the deep learning model in general. The comprehensive experiments in the study were performed on the YOLOv7 model to test and evaluate the robustness of the YOLOv7 model, which is also a popularly used deep learning model and is considered to be advanced today.