Adversarial Attack on YOLO Neural Network

This paper describes and demonstrates a comprehensive analysis of structured criteria of formalized conditions for creating universal images falsely classified by computer vision algorithms called adversarial examples based on YOLO neural network models. In this paper, a pattern was identified and studied using the above mathematical model of the proposed algorithm for the successful creation of a universal destructive image depending on the generated dataset, on which neural networks were trained using a fast sign gradient attack. This pattern is demonstrated for YOLO 8, YOLO 9, YOLO 10, and YOLO 11 classifier models trained on the basis of the standard COCO dataset.