The increasing sophistication of image manipulation techniques in the digital era has raised urgent concerns about the authenticity and reliability of digital images, particularly in sensitive fields such as journalism, legal investigations, and digital content verification. One of the most prevalent forms of manipulation is copy-move forgery, where a region of an image is copied and pasted elsewhere within the same image to conceal or duplicate content. In response to this challenge, this study proposes a novel deep learning-based methodology that integrates multiscale feature extraction, DBSCAN-based superpixel segmentation, and depth reconstruction to effectively detect copy-move forgeries. The proposed system utilizes the VGGNet-16 architecture for robust feature extraction and combines it with spatial depth cues to uncover subtle inconsistencies introduced during tampering. The key contributions of this work include the integration of density-based clustering for localized forgery region identification, the application of depth estimation to improve spatial analysis, and a modular framework that enhances both detection accuracy and computational efficiency. Experimental evaluation conducted on the CASIAv2 dataset demonstrates that the proposed approach achieves superior performance compared to state-of-the-art methods, with notable improvements in precision, recall, and F1-score—even in the presence of post-processing noise and geometric transformations. Additionally, the system maintains low computational overhead, making it suitable for real-time or large-scale forensic applications. This work lays the foundation for extending the methodology to detect other complex manipulations such as image splicing and multiple cloning, offering a scalable and reliable solution to uphold digital image integrity in an increasingly manipulated media landscape.