Nowadays, people in society are increasingly depending on multimedia content, particularly digital images and videos, as reliable evidence of events. However, with the accessibility of advanced and easy-to-use video editing tools, even beginners can easily alter digital video content, which could be used as evidence in digital investigations. This raises significant concerns about the authenticity of digital videos. This study presents a method for detecting video forgery using a Depth-Wise Convolutional Neural Network (DWCNN) model that specifically crafted to precisely identify and detect forged videos. The proposed approach processes both forged and original video datasets, extracting individual frames for analysis. Ground truth data is utilized to label frames as forged or non-forged, based on pixel-level annotations. A CNN model is trained on these frames to classify forged and authentic video content. The model has a validation accuracy of 99.5%, demonstrating its effectiveness in identifying tampered videos. This high accuracy highlights the potential of deep learning techniques for robust video forgery detection in digital investigations.