The paper presents a framework on deep learning based on the following areas-authentication and detection of tampering of medical images with growing concerns in the area of visual image analysis, that can be secured and reliable in the health sector. The proposed method is based on the use of convolutional neural networks to address issues on the validity and integrity of medical images with accuracy in avoiding unauthorized modifications. The suggested approach combines image preprocessing, feature extraction, and an accurate module on tampering detection for determining whether medical images are forged or altered. Thus, the model has been experimented on a number of publicly available datasets of medical images and obtained an authentication accuracy of 97.5% and a tampering detection rate of 95.8%. The results obtained indicate that this model beats the existing techniques with respect to performance, and its potentials are enormous for improving security in systems used for medical images, including forensic analysis and healthcare security.