Enhancing Object Detection in YouTube Thumbnails Forensics with YOLOv4

YouTube thumbnails play a vital role as visual indicators, succinctly capturing the essence of a video alongside its title and description. Beyond mere previews, these thumbnails have evolved into significant digital artifacts with implications for disk image encryption. This research delves into potentially integrating the advanced YOLOv4 (You Only Look Once) algorithm into creating YouTube thumbnails. YOLOv4 enhances the process by automatically identifying and emphasizing objects of interest in these visual previews. This paper diversifies the dataset to improve the model’s effectiveness, expanding its capacity to recognize and highlight objects more effectively. We address data security challenges by broadening the training data, incorporating authentication, and decrypting the dataset to align it with real-world thumbnail images. The primary objective is to assess the efficacy of YOLOv4 object detection models in authenticated YouTube thumbnail videos. The network underwent training to recognize 80 object classes, achieving a 90% prediction rate and a 92% confidence rate.