With rapid advancements of the Internet of Vehicles (IoV), securing vehicular communications becomes highly important. In this paper, a visual fingerprint update algorithm is proposed for smart IoV systems by relying on crowdsourced localization and deep learning. The vehicles utilize their sensors to acquire some location data to be transmitted to the cloud, and a framework is developed. This, therefore, justifies creating a real-time localization database that accurately produces the localization database. In such a design, using visual data from vehicles, the model is deep learned in a way that learns characteristics about locations and generates visual fingerprints for security purposes. In this case, the generated visual fingerprints update regularly to ensure reliability. The algorithm has been rigorously tested on real-world data; its performance in vehicle localization and visual fingerprint updating is accurate, and it also robustly defends against environmental challenges such as lighting and weather changes. The crowdsourcing and deep learning-based updating algorithm of visual fingerprint enhances the security system of smart IoV. The real-time update flexibility is provided by the algorithm. Algorithmic model updates enable it to adjust and adapt more dynamically to changing road conditions, thus making false positives or negatives in authenticating the identity minimal and enhancing vehicular communication security.